Probabilistic Optimizations

I'm going to say it straight up: all neural networks are probabilistic algorithms. The entire concept of a neural network is a probabilistic approximation of reality based on what it's seen during training. In that sense, an AI engine is one big probabilistic algorithm, and optimizing it with another low-level probabilistic algorithm is really creating a doubly-probabilistic algorithm. After all, conceptually speaking, the result of an AI engine's output, called "logits", are probabilities of the likelihood of outputting a particular word. The core functionality of an AI model is to train it so it knows what words are "likely", and then it becomes good enough to "guestimate" what words should be output based on the probabilities that it calculates.

Similar to probabilistic algorithms are "stochastic algorithms", where an amount of randomness is injected to give results. For example, in the core theory of Computer Science, the "Monte Carlo method" is a well-known algorithm for measuring the area under a curve using randomly generated points. Some of these concepts can also be applied to AI models. These stochastic algorithms are technically a subset of probabilistic algorithms, since there are probabilistic methods that do not require random numbers, but still rely on probabilities for correct execution (e.g. neural networks). An example of a stochastic algorithm for neural networks is "stochastic quantization", where intentional randomness attempts to improve a quantized model.

The main use of probabilistic algorithms in AI inference is the randomness injected into decoding algorithms with the top-k, top-p, and temperature hyper-parameters. The randomness in these methods ensure that the AI engine doesn't output the exact same response when it receives the same input, so it can show creativity and originality in its responses to prompts. Note that the goal of randomness in decoding algorithms is variety of responses, and does not improve the speed of inference. However, the final stage of decoding is rarely a bottleneck, so the randomness doesn't degrade latency either.

Randomness of stochastic algorithms is not always beneficial. It can be useful to turn off the randomness in these decoding algorithms when regression testing an AI engine, which is a situation where having the exact same responses can be useful.

Probabilistic Optimizations

Probabilistic optimizations are methods of speeding up inference based on the relative probabilities of events. There are both high-level and lower-level ways that neural networks can use probabilistic algorithms. There are two main types of probabilistic algorithms:

• Common case first. These are optimizations where a likely case is handled first, using fast code, but the unlikely case is also handled as a backup, with slower code. In such cases, the probabilistic algorithm does not affect the result of the neural network, only its speed.
• Lossy or error-accepting algorithms. These are probabilistic algorithms where the unlikely case is simply ignored, and an error in such cases is accepted as part of the logic. Such algorithms may be appropriate for neural networks because they have an inherent resilience to errors.

Arguably, many of the inference optimization techniques and almost all model compression optimizations are lossy probabilistic optimizations, where a known level of error is tolerated. They take the probabilistic AI engine, add some known extra error sources to it (but making it faster), and the result is a faster, but slightly less accurate model. Examples of such "probabilistic optimizations" that offer trade-offs at a high-level are techniques such as:

• Quantization (accepts errors resulting from the loss of precision).
• Early exits (assumes that later layers won't change results, tolerating cases where this is erroneous).
• Magnitude pruning (assumes that low-value weights have little impact, accepting the resulting errors).
• Big-little models (common case first method; see ensemble algorithms)
• Approximate algorithms (various types)

Some examples of the lower-level specific uses of probabilistic algorithms in neural networks include:

• Bloom filters
• Stochastic quantization
• Approximate arithmetic multiplication algorithms
• Approximate matrix multiplication algorithms
• Loop perforation

Probabilistic Algorithm Research Papers

General research papers on specific probabilistic algorithms (including stochastic algorithms) in neural networks:

• Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang, 2017, "SC-DCNN: Highly-scalable deep convolutional neural network using stochastic computing", ACM SIGPLAN Notices, vol. 52, no. 4, pp. 405-418, 2017. https://arxiv.org/abs/1611.05939 (Stochastic method with multiplication and addition approximations via AND gates and multiplexers.)
• R. Hojabr et al., "SkippyNN: An embedded stochastic-computing accelerator for convolutional neural networks", Proc. 56th ACM/IEEE Design Automat. Conf. (DAC), pp. 1-6, Jun. 2019. https://ieeexplore.ieee.org/document/8806970
• Nikola Popovic, Danda Pani Paudel, Thomas Probst, Luc Van Gool, July 2022, Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers, https://arxiv.org/abs/2112.15111
• N Popovic, DP Paudel, T Probst, L Van Gool, 2023, Token-Consistent Dropout For Calibrated Vision Transformers, 2023 IEEE International Conference on Image Processing (ICIP), https://ieeexplore.ieee.org/abstract/document/10222084
• Carlos Florensa, Yan Duan, Pieter Abbeel, April 2017, Stochastic Neural Networks for Hierarchical Reinforcement Learning, arXiv preprint arXiv:1704.03012, 2017, https://arxiv.org/abs/1704.03012
• E Wong, 1991, Stochastic neural networks, Algorithmica, Springer https://link.springer.com/article/10.1007/BF01759054, https://arxiv.org/pdf/1704.03012.pdf
• Roohi Menon, Hangyu Zhou, Gerald Ogbonna, Vikram Raghavan, 2021, Stochastic programming, SYSEN 6800 Fall 2021, Cornell University Computational Optimization Open Textbook, https://optimization.cbe.cornell.edu/index.php?title=Stochastic_programming
• John R. Birge , François Louveaux, 2011, Introduction to Stochastic Programming, Springer Series in Operations Research and Financial Engineering (ORFE), https://link.springer.com/book/10.1007/978-1-4614-0237-4
• Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Ishan Thakkar, Ahmad Salehi, Todd Hastings, Feb 2023, SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs, https://arxiv.org/abs/2302.07036, Code: https://github.com/uky-UCAT/SC_ONN_SIM.git
• Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin. 2020. Spatially adaptive inference with stochastic feature sampling and interpolation. arXiv preprint arXiv:2003.08866, https://arxiv.org/abs/2003.08866
• Bengio, Yoshua, Leonard, Nicholas, and Courville, Aaron, 2013, Estimating or propagating gradients ´ through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, https://arxiv.org/abs/1308.3432
• Hanting Chen, Zhicheng Liu, Xutao Wang, Yuchuan Tian, Yunhe Wang, 1 Apr 2024 (v2), DiJiang: Efficient Large Language Models through Compact Kernelization, https://arxiv.org/abs/2403.19928 (Using the Monte Carlo method to achieve a linear attention approximation.)
• Salar Shakibhamedan, Amin Aminifar, Nima TaheriNejad, Axel Jantsch, 2024, EASE: Energy Optimization through Adaptation — A Review of Runtime Energy-Aware Approximate Deep Learning Algorithms, https://eclectx.org/Publications/2024_M13.pdf (Survey paper on techniques for adaptive inference with a focus on approximations of inference, including loop performance, stochastic algorithms, approximate arithmetic, quantization, pruning and low-rank.)
• Jing Yang Lee, Kong Aik Lee, Woon-Seng Gan, Nov 2023, Partially Randomizing Transformer Weights for Dialogue Response Diversity, https://arxiv.org/abs/2311.10943
• Zhengmian Hu, Heng Huang, 2024, Accelerated Speculative Sampling Based on Tree Monte Carlo, Proceedings of the 41st International Conference on Machine Learning, PMLR 235:19216-19251, 2024. https://proceedings.mlr.press/v235/hu24f.html https://openreview.net/forum?id=stMhi1Sn2G PDF: https://raw.githubusercontent.com/mlresearch/v235/main/assets/hu24f/hu24f.pdf
• Lingyun Yao, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang, Karthekeyan Periasamy, Martin Andraud, 22 May 2024, On Hardware-efficient Inference in Probabilistic Circuits, https://arxiv.org/abs/2405.13639
• Wenlun Zhang, Shimpei Ando, Yung-Chin Chen, Satomi Miyagi, Shinya Takamaeda-Yamazaki, Kentaro Yoshioka, 29 Aug 2024, PACiM: A Sparsity-Centric Hybrid Compute-in-Memory Architecture via Probabilistic Approximation, https://arxiv.org/abs/2408.16246
• Haiyue Ma, Jian Liu, Ronny Krashinsky, 10 Oct 2024, Reducing the Cost of Dropout in Flash-Attention by Hiding RNG with GEMM, https://arxiv.org/abs/2410.07531
• Felix Jimenez, Matthias Katzfuss, 8 Jan 2025, Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks, https://arxiv.org/abs/2501.04816
• Andreas Krause, Jonas Hübotter, 7 Feb 2025, Probabilistic Artificial Intelligence, https://arxiv.org/abs/2502.05244
• S Xu, L Jiayao, Z He, C Peng, W Xu, Mar 2025, The Surprising Effectiveness of Randomness in LLM Pruning, https://openreview.net/pdf?id=YncWrbIxnN https://anonymous.4open.science/r/random-prune-8F1C
• Jacob Trauger, Ambuj Tewari, 16 May 2025, On Next-Token Prediction in LLMs: How End Goals Determine the Consistency of Decoding Algorithms, https://arxiv.org/abs/2505.11183
• Yuchen Zhu, Wei Guo, Jaemoo Choi, Guan-Horng Liu, Yongxin Chen, Molei Tao, 14 Aug 2025, MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control, https://arxiv.org/abs/2508.10684
• Jung-hun Kim and Milan Vojnovic, 14 Aug 2025, Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards, https://arxiv.org/abs/2112.06362
• Tianyi Wang, Bingqian Dai, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, and Kang L. Wang, 23 Jul 2025, Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics, https://arxiv.org/abs/2507.17193
• El Mahdi Chayti and Nikita Doikov and Martin Jaggi, 23 Jul 2025, Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods, https://arxiv.org/abs/2302.11962
• Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu, 14 Aug 2025, PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning, https://arxiv.org/abs/2508.10501
• Weijian Mai, Jiamin Wu, Yu Zhu, Zhouheng Yao, Dongzhan Zhou, Andrew F. Luo, Qihao Zheng, Wanli Ouyang, Chunfeng Song, 14 Aug 2025, SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning, https://arxiv.org/abs/2508.10298
• Isha Puri, Shivchander Sudalairaj, Guangxuan Xu, Kai Xu and Akash Srivastava, 14 Aug 2025, Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods, https://arxiv.org/abs/2502.01618
• Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin, 14 Aug 2025, Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models, https://arxiv.org/abs/2506.13061
• Pengjiu Xia, Yidian Huang, Wenchao Wei, Yuwen Tan, 22 Jul 2025, Confidence Optimization for Probabilistic Encoding, https://arxiv.org/abs/2507.16881
• Jacqueline Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov, 23 Jul 2025, Probabilistic Graphical Models: A Concise Tutorial, https://arxiv.org/abs/2507.17116
• Songxuan Shi, 23 Jul 2025, Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions, https://arxiv.org/abs/2507.17255
• Aritz P\'erez, Carlos Echegoyen and Guzm\'an Santaf\'e, 23 Jul 2025, Decentralized Federated Learning of Probabilistic Generative Classifiers, https://arxiv.org/abs/2507.17285
• Damiano Azzolini, Fabrizio Riguzzi, Theresa Swift, 23 Jul 2025, Integrating Belief Domains into Probabilistic Logic Programs, https://arxiv.org/abs/2507.17291
• Leandro Von Krannichfeldt, Kristina Orehounig and Olga Fink, 23 Jul 2025, Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling, https://arxiv.org/abs/2507.17526
• Mohammad Taha Askari, Lutz Lampe, and Amirhossein Ghazisaeidi, 21 Jul 2025, Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications, https://arxiv.org/abs/2507.16012
• Lars Hillebrand, David Biesner, Christian Bauckhage, Rafet Sifa, 22 Jul 2025, Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM, https://arxiv.org/abs/2507.16695
• Minglei Yang, Yanfang Liu, Diego del-Castillo-Negrete, Yanzhao Cao, Guannan Zhang, 17 Jul 2025, Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains, https://arxiv.org/abs/2507.15990
• Arseniy Andreyev and Pierfrancesco Beneventano, 22 Jul 2025, Edge of Stochastic Stability: Revisiting the Edge of Stability for SGD, https://arxiv.org/abs/2412.20553
• Linxuan He, Qing-Shan Jia, Ang Li, Hongyan Sang, Ling Wang, Jiwen Lu, Tao Zhang, Jie Zhou, Yi Zhang, Yisen Wang, Peng Wei, Zhongyuan Wang, Henry X. Liu, and Shuo Feng, 22 Jul 2025, Towards provable probabilistic safety for scalable embodied AI systems, https://arxiv.org/abs/2506.05171
• Guanyi Chen, Jian Ding, Shuyang Gong, Zhangsong Li, 22 Jul 2025, A computational transition for detecting correlated stochastic block models by low-degree polynomials, https://arxiv.org/abs/2409.00966
• Kuangyu Ding and Kim-Chuan Toh, 22 Jul 2025, On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem, https://arxiv.org/abs/2507.15264
• Wonjun Jeong, Dongseok Kim, Taegkeun Whangbo, 24 Jul 2025, SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models, https://arxiv.org/abs/2507.18182
• Masaki Adachi, Masahiro Fujisawa, Michael A Osborne, 24 Jul 2025, Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature, https://arxiv.org/abs/2503.06079
• Jon Vadillo, Roberto Santana, Jose A. Lozano, Marta Kwiatkowska, 18 Jul 2025, Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks, https://arxiv.org/abs/2403.13740
• Thom Badings, Wietze Koops, Sebastian Junges, Nils Jansen, 18 Jul 2025, Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates, https://arxiv.org/abs/2406.00826
• Dirk Tasche, 18 Jul 2025, Recalibrating binary probabilistic classifiers, https://arxiv.org/abs/2505.19068
• Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, Alexander Keller, 18 Jul 2025, FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale, https://arxiv.org/abs/2507.12144
• Lionel Wong, Katherine M. Collins, Lance Ying, Cedegao E. Zhang, Adrian Weller, Tobias Gerstenberg, Timothy O'Donnell, Alexander K. Lew, Jacob D. Andreas, Joshua B. Tenenbaum, Tyler Brooke-Wilson, 18 Jul 2025, Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models, https://arxiv.org/abs/2507.12547
• Mykhailo Buleshnyi, Anna Polova, Zsolt Zombori, Michael Benedikt, 20 Jul 2025, Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification, https://arxiv.org/abs/2507.15156
• Owen Douglas and Aku Kammonen and Anamika Pandey and Ra\'ul Tempone, 21 Jul 2025, An Adaptive Random Fourier Features approach Applied to Learning Stochastic Differential Equations, https://arxiv.org/abs/2507.15442
• Chen Chen, Lijin Wang, Yanzhao Cao, Xupeng Cheng, 19 Jul 2025, Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network, https://arxiv.org/abs/2507.14467
• Roberto Miele, Niklas Linde, 21 Jul 2025, Diffusion models for multivariate subsurface generation and efficient probabilistic inversion, https://arxiv.org/abs/2507.15809
• Anthony M. Polloreno, 20 Jul 2025, Restrictions on Physical Stochastic Reservoir Computers, https://arxiv.org/abs/2307.14474
• Kenny Falk{\ae}r Olsen, Mads {\O}stergaard, Karl Ulb{\ae}k, S{\o}ren F{\o}ns Nielsen, Rasmus Malik H{\o}egh Lindrup, Bj{\o}rn Sand Jensen, Morten M{\o}rup, 20 Jul 2025, Knowing When to Quit: Probabilistic Early Exits for Speech Separation, https://arxiv.org/abs/2507.09768
• Jaikrishna Manojkumar Patil, Nathaniel Lee, Al Mehdi Saadat Chowdhury, YooJung Choi, Paulo Shakarian, 8 Aug 2025, Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets, https://arxiv.org/abs/2508.06706
• Yuhao Liu, Rui Hu, Yu Chen, Longbo Huang, 10 Aug 2025, Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants, https://arxiv.org/abs/2508.07333
• Martin Rektoris, Milan Pape\v{z}, V\'aclav \v{S}m\'idl, Tom\'a\v{s} Pevn\'y, 11 Aug 2025, Sparse Probabilistic Graph Circuits, https://arxiv.org/abs/2508.07763
• Christos Tsirigotis, Vaibhav Adlakha, Joao Monteiro, Aaron Courville, Perouz Taslakian, 9 Aug 2025, BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation, https://arxiv.org/abs/2508.06781
• Toan Huynh, Ruth Lopez Fajardo, Guannan Zhang, Lili Ju, Feng Bao, 9 Aug 2025, A Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions, https://arxiv.org/abs/2508.06834
• Juan-Pablo Ortega and Florian Rossmannek, 11 Aug 2025, Stochastic dynamics learning with state-space systems, https://arxiv.org/abs/2508.07876
• Bogdan Butyrin, Artemy Rubtsov, Alexey Naumov, Vladimir Ulyanov, Sergey Samsonov, 11 Aug 2025, Gaussian Approximation for Two-Timescale Linear Stochastic Approximation, https://arxiv.org/abs/2508.07928
• Martin Rouault, R\'emi Bardenet, Myl\`ene Ma\"ida, 9 Aug 2025, Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding, https://arxiv.org/abs/2402.11736
• \'Isak P\'etursson and Mar\'ia \'Oskarsd\'ottir, 11 Aug 2025, Chaos into Order: Neural Framework for Expected Value Estimation of Stochastic Partial Differential Equations, https://arxiv.org/abs/2502.03670
• Adrien Cort\'es, R\'emi Rehm, Victor Letzelter, 11 Aug 2025, Winner-takes-all for Multivariate Probabilistic Time Series Forecasting, https://arxiv.org/abs/2506.05515
• Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei, 10 Aug 2025, Probabilistic Optimality for Inference-time Scaling, https://arxiv.org/abs/2506.22376
• Aliakbar Nafar, Kristen Brent Venable, Zijun Cui, Parisa Kordjamshidi, 10 Aug 2025, Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterization, https://arxiv.org/abs/2505.15918
• Liu junkang and Yuanyuan Liu and Fanhua Shang and Hongying Liu and Jin Liu and Wei Feng, 26 Jul 2025, FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging, https://arxiv.org/abs/2507.20016
• Rezaur Rashid, Gabriel Terejanu, 27 Jul 2025, From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery, https://arxiv.org/abs/2507.20349
• Aditya Ravuri, Neil D. Lawrence, 28 Jul 2025, Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements, https://arxiv.org/abs/2507.21040
• Xintao Xia, Linjun Zhang, Zhanrui Cai, 28 Jul 2025, Statistical Inference for Differentially Private Stochastic Gradient Descent, https://arxiv.org/abs/2507.20560
• Yuhao Liu, Yu Chen, Rui Hu and Longbo Huang, 28 Jul 2025, Finite-Time Analysis of Discrete-Time Stochastic Interpolants, https://arxiv.org/abs/2502.09130
• Shenglong Zhou, Ouya Wang, Ziyan Luo, Yongxu Zhu, Geoffrey Ye Li, 26 Jul 2025, Preconditioned Inexact Stochastic ADMM for Deep Model, https://arxiv.org/abs/2502.10784
• Xingjian Wu, Xiangfei Qiu, Hongfan Gao, Jilin Hu, Bin Yang, Chenjuan Guo, 26 Jul 2025, $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting, https://arxiv.org/abs/2505.23017
• Swagatam Das, 27 Jul 2025, A Free Probabilistic Framework for Analyzing the Transformer-based Language Models, https://arxiv.org/abs/2506.16550
• Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting, 26 Jul 2025, Tractable Representation Learning with Probabilistic Circuits, https://arxiv.org/abs/2507.04385
• Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran Tchrakian, Javier Carnerero Cano, Yufang Hou, Elizabeth Daly, Alessandra Pascale, 26 Jul 2025, FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models, https://arxiv.org/abs/2502.18573
• Sara Pohland and Claire Tomlin, 26 Jul 2025, PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification, https://arxiv.org/abs/2411.16715
• Chenyuan Zhang, Cristian Rojas Cardenas, Hamid Rezatofighi, Mor Vered, Buser Say, 29 Jul 2025, Probabilistic Active Goal Recognition, https://arxiv.org/abs/2507.21846
• Raffaele Pojer, Andrea Passerini, Kim G. Larsen, Manfred Jaeger, 29 Jul 2025, A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data, https://arxiv.org/abs/2507.21873
• Pedram Rajaei, Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari, Ali Yousefi, 29 Jul 2025, Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series, https://arxiv.org/abs/2507.21531
• Paul Patrone and Anthony Kearsley, 29 Jul 2025, Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification, https://arxiv.org/abs/2507.21670
• Daniele Lanzoni and Olivier Pierre-Louis and Roberto Bergamaschini and Francesco Montalenti, 29 Jul 2025, Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks, https://arxiv.org/abs/2507.21763
• Satoshi Kumabe, Tianyu Song, Ton Viet Ta, 29 Jul 2025, Stochastic forest transition model dynamics and parameter estimation via deep learning, https://arxiv.org/abs/2507.21486
• Vasileios Manginas, Nikolaos Manginas, Edward Stevinson, Sherwin Varghese, Nikos Katzouris, Georgios Paliouras, Alessio Lomuscio, 29 Jul 2025, A Scalable Approach to Probabilistic Neuro-Symbolic Robustness Verification, https://arxiv.org/abs/2502.03274
• Antonio Lorenzin, Fabio Zanasi, 28 Jul 2025, An Algebraic Approach to Moralisation and Triangulation of Probabilistic Graphical Models, https://arxiv.org/abs/2503.11820
• Yueyang Yao, Jiajun Li, Xingyuan Dai, MengMeng Zhang, Xiaoyan Gong, Fei-Yue Wang, Yisheng Lv, 29 Jul 2025, Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting, https://arxiv.org/abs/2505.10774
• Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson, 29 Jul 2025, Probabilistic Directed Distance Fields for Ray-Based Shape Representations, https://arxiv.org/abs/2404.09081
• Tom Liu, Anna Wu, Chao Li, 29 Jul 2025, Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling, https://arxiv.org/abs/2503.22745
• Haoyu Wang and Chris M. Poskitt and Jun Sun and Jiali Wei, 1 Aug 2025, Pro2Guard: Proactive Runtime Enforcement of LLM Agent Safety via Probabilistic Model Checking, https://arxiv.org/abs/2508.00500
• Leonidas Akritidis, Panayiotis Bozanis, 1 Aug 2025, A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces, https://arxiv.org/abs/2508.00472
• Liuyun Xu, Seymour M.J. Spence, 1 Aug 2025, Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems, https://arxiv.org/abs/2508.00734
• Molly Noel, Gabriel Mancino-Ball, Yangyang Xu, 1 Aug 2025, Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks, https://arxiv.org/abs/2508.00267
• Matteo Bergamaschi, Andrea Cristofari, Vyacheslav Kungurtsev, and Francesco Rinaldi, 1 Aug 2025, Probabilistic Iterative Hard Thresholding for Sparse Learning, https://arxiv.org/abs/2409.01413
• Etienne Buehrle and Christoph Stiller, 26 Jul 2025, Stochastic Optimal Control via Measure Relaxations, https://arxiv.org/abs/2508.00886
• Siddharth Rout, Eldad Haber, Stephane Gaudreault, 1 Aug 2025, Flow Matching for Probabilistic Learning of Dynamical Systems from Missing or Noisy Data, https://arxiv.org/abs/2508.01101
• Joshua Dimasaka, Christian Gei{\ss}, Emily So, 2 Aug 2025, GraphVSSM: Graph Variational State-Space Model for Probabilistic Spatiotemporal Inference of Dynamic Exposure and Vulnerability for Regional Disaster Resilience Assessment, https://arxiv.org/abs/2508.01310
• Yeongjong Kim, Yeoneung Kim, Minseok Kim, Namkyeong Cho, 3 Aug 2025, Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach, https://arxiv.org/abs/2508.01718
• Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi, Xu Zheng, Dongsheng Luo, 3 Aug 2025, From Binary to Continuous: Stochastic Re-Weighting for Robust Graph Explanation, https://arxiv.org/abs/2508.01925
• Alexander Norcliffe, Changhee Lee, Fergus Imrie, Mihaela van der Schaar, Pietro Lio, 3 Aug 2025, Stochastic Encodings for Active Feature Acquisition, https://arxiv.org/abs/2508.01957
• V\"ain\"o Yrj\"an\"ainen, Isac Bostr\"om, M{\aa}ns Magnusson and Johan Jonasson, 4 Aug 2025, Posterior Sampling of Probabilistic Word Embeddings, https://arxiv.org/abs/2508.02337
• Yinbin Han, Meisam Razaviyayn, Renyuan Xu, 3 Aug 2025, Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence, https://arxiv.org/abs/2412.18164
• Christian Wald and Gabriele Steidl, 2 Aug 2025, Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans, https://arxiv.org/abs/2501.16839
• Helin Cao and Sven Behnke, 2 Aug 2025, DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models, https://arxiv.org/abs/2409.18092
• Tasnia Nobi Afee, Jack Hutchins, Md Mazharul Islam, Thomas Kampfe and Ahmedullah Aziz, 2 Aug 2025, Embedding-Enhanced Probabilistic Modeling of Ferroelectric Field Effect Transistors (FeFETs), https://arxiv.org/abs/2508.02737
• Eliot Beyler (SIERRA), Francis Bach (SIERRA), 5 Aug 2025, Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance, https://arxiv.org/abs/2508.03210
• Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon, 5 Aug 2025, UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression, https://arxiv.org/abs/2508.03520
• Ling-Qi Zhang, Zahra Kadkhodaie, Eero P. Simoncelli, David H. Brainard, 4 Aug 2025, Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models, https://arxiv.org/abs/2405.17456
• Chengcheng Yan, Jiawei Xu, Zheng Peng, Qingsong Wang, 6 Aug 2025, Neural Network Training via Stochastic Alternating Minimization with Trainable Step Sizes, https://arxiv.org/abs/2508.04193
• Justin Lee, Behnaz Moradijamei, Heman Shakeri, 6 Aug 2025, Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points, https://arxiv.org/abs/2508.04351
• Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo, Eric Vanden-Eijnden, 6 Aug 2025, Multitask Learning with Stochastic Interpolants, https://arxiv.org/abs/2508.04605
• Wei Liu, Anweshit Panda, Ujwal Pandey, Christopher Brissette, Yikang Shen, George M. Slota, Naigang Wang, Jie Chen, Yangyang Xu, 7 Aug 2025, Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization, https://arxiv.org/abs/2508.04950
• Hikaru Umeda, Hideaki Iiduka, 7 Aug 2025, Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity, https://arxiv.org/abs/2508.05302
• Hrithik Suresh, Sahil Sidheekh, Vishnu Shreeram M.P, Sriraam Natarajan, Narayanan C. Krishnan, 7 Aug 2025, Tractable Sharpness-Aware Learning of Probabilistic Circuits, https://arxiv.org/abs/2508.05537
• Ashok Cutkosky, Harsh Mehta, Francesco Orabona, 7 Aug 2025, Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion, https://arxiv.org/abs/2302.03775
• Helen Jin, Anton Xue, Weiqiu You, Surbhi Goel, Eric Wong, 7 Aug 2025, Probabilistic Stability Guarantees for Feature Attributions, https://arxiv.org/abs/2504.13787
• Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee, Krishnateja Killamsetty, Nikhil Shivakumar Nayak, Ganesh Ramakrishnan, 7 Aug 2025, Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning, https://arxiv.org/abs/2507.12612
• Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, 7 Aug 2025, Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation, https://arxiv.org/abs/2410.05106
• Haley Stone, Jing Du, Hao Xue, Matthew Scotch, David Heslop, Andreas Z\"ufle, Chandini Raina MacIntyre, Flora Salim, 7 Aug 2025, A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models, https://arxiv.org/abs/2506.09076
• Alex Glushkovsky, 8 Aug 2025, Dual Signal Decomposition of Stochastic Time Series, https://arxiv.org/abs/2508.05915
• Zichun Ye, Runqi Wang, Xutong Liu, Shuai Li, 8 Aug 2025, Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits, https://arxiv.org/abs/2508.06247
• Arvind K. Saibaba and Ilse C.F. Ipsen, 7 Aug 2025, Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory, https://arxiv.org/abs/2508.05764
• Fran\c{c}ois Bachoc and Nicol\`o Cesa-Bianchi and Tommaso Cesari and Roberto Colomboni, 7 Aug 2025, Stochastic Bandits for Crowdsourcing and Multi-Platform Autobidding, https://arxiv.org/abs/2508.05844
• Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian, 8 Aug 2025, Probabilistic Foundations for Metacognition via Hybrid-AI, https://arxiv.org/abs/2502.05398
• Kevin Pekepok, Persephone Kirkwood, Esme Christopolous, Florence Braithwaite, and Oliver Nightingale, 8 Aug 2025, Hierarchical Pattern Decryption Methodology for Ransomware Detection Using Probabilistic Cryptographic Footprints, https://arxiv.org/abs/2501.15084
• Joshua R. Tempelman, Kevin Mitchell, Adam J. Wachtor, Eric B. Flynn, 5 Aug 2025, Probabilistic Emissivity Retrieval from Hyperspectral Data via Physics-Guided Variational Inference, https://arxiv.org/abs/2508.08291
• Jiani Ni, He Zhao, Yibo Yang, Dandan Guo, 12 Aug 2025, Deep Neural Network Calibration by Reducing Classifier Shift with Stochastic Masking, https://arxiv.org/abs/2508.09116
• Shady Agwa, Yihan Pan, Georgios Papandroulidakis and Themis Prodromakis, 12 Aug 2025, OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads, https://arxiv.org/abs/2508.08822
• Jialiang Shi, Yaguang Dou, Tian Qi, 12 Aug 2025, SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system, https://arxiv.org/abs/2508.09090
• Johannes Aspman, Vyacheslav Kungurtsev, Reza Roohi Seraji, 12 Aug 2025, Tame Riemannian Stochastic Approximation, https://arxiv.org/abs/2302.00709
• Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na, 12 Aug 2025, Online Covariance Estimation in Nonsmooth Stochastic Approximation, https://arxiv.org/abs/2502.05305
• Ziheng Wang, Pedro Reviriego, Farzad Niknia, Zhen Gao, Javier Conde, Shanshan Liu, Fabrizio Lombardi, 5 Aug 2025, Energy-Efficient Stochastic Computing (SC) Neural Networks for Internet of Things Devices With Layer-Wise Adjustable Sequence Length (ASL), https://arxiv.org/abs/2508.09163
• Yao Li, Yicheng Liu, Shirou Wang, 13 Aug 2025, DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems, https://arxiv.org/abs/2508.09529
• Akshay Thakur and Sawan Kumar and Matthew Zahr and Souvik Chakraborty, 13 Aug 2025, Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems, https://arxiv.org/abs/2508.09623
• Arpit Agarwal and Rohan Ghuge and Viswanath Nagarajan and Zhengjia Zhuo, 13 Aug 2025, Semi-Bandit Learning for Monotone Stochastic Optimization, https://arxiv.org/abs/2312.15427
• Taihei Oki, Shinsaku Sakaue, 13 Aug 2025, No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting, https://arxiv.org/abs/2405.12439
• Magdalena Tr\k{e}dowicz, Marcin Mazur, Szymon Janusz, Arkadiusz Lewicki, Jacek Tabor, {\L}ukasz Struski, 13 Aug 2025, PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification, https://arxiv.org/abs/2406.11443
• Arnab Ganguly, Riten Mitra, Jinpu Zhou, 15 Aug 2025, Nonparametric learning of stochastic differential equations from sparse and noisy data, https://arxiv.org/abs/2508.11597
• Jinwen Xu, Qin Lu, Yaakov Bar-Shalom, 14 Aug 2025, Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems, https://arxiv.org/abs/2503.06381
• Michael Bereket and Jure Leskovec, 15 Aug 2025, Uncalibrated Reasoning: GRPO Induces Overconfidence for Stochastic Outcomes, https://arxiv.org/abs/2508.11800
• Denis Blessing and Julius Berner and Lorenz Richter and Carles Domingo-Enrich and Yuanqi Du and Arash Vahdat and Gerhard Neumann, 17 Aug 2025, Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference, https://arxiv.org/abs/2508.12511
• Marcos Abel Zuzu\'arregui, Stefano Carpin, 17 Aug 2025, Solving Stochastic Orienteering Problems with Chance Constraints Using a GNN Powered Monte Carlo Tree Search, https://arxiv.org/abs/2409.04653
• Emma Hannula, Arttu H\"akkinen, Antti Solonen, Felipe Uribe, Jana de Wiljes, Lassi Roininen, 18 Aug 2025, Partially stochastic deep learning with uncertainty quantification for model predictive heating control, https://arxiv.org/abs/2504.03350
• Wenhao Mu, Zhi Cao, Mehmed Uludag, Alexander Rodr\'iguez, 18 Aug 2025, Counterfactual Probabilistic Diffusion with Expert Models, https://arxiv.org/abs/2508.13355
• Zhuang Yang, 19 Aug 2025, Explainable Learning Rate Regimes for Stochastic Optimization, https://arxiv.org/abs/2508.13639
• Soumyajit Guin and Shalabh Bhatnagar, 19 Aug 2025, Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem, https://arxiv.org/abs/2508.13963
• Yiyang Jia, Zheng Wei, Zheng Yang, Guohong Peng, 16 Aug 2025, Modeling GRNs with a Probabilistic Categorical Framework, https://arxiv.org/abs/2508.13208
• Vladimir Berman, 18 Aug 2025, Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures, https://arxiv.org/abs/2508.13237
• Federico Nicolas Peccia, Frederik Haxel, Oliver Bringmann, 19 Aug 2025, Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs, https://arxiv.org/abs/2507.01457
• Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler, 19 Aug 2025, Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging, https://arxiv.org/abs/2005.00124
• Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov, 19 Aug 2025, Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent, https://arxiv.org/abs/2502.06719
• Swantje Plambeck, Ali Salamati, Eyke Huellermeier, Goerschwin Fey, 20 Aug 2025, Data-Driven Probabilistic Evaluation of Logic Properties with PAC-Confidence on Mealy Machines, https://arxiv.org/abs/2508.14710
• Junwei Su, Chuan Wu, 20 Aug 2025, A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations, https://arxiv.org/abs/2508.14351
• Junwei Su, Shan Wu, 20 Aug 2025, SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion, https://arxiv.org/abs/2508.14352
• Sridhar Mahadevan, 20 Aug 2025, Universal Reinforcement Learning in Coalgebras: Asynchronous Stochastic Computation via Conduction, https://arxiv.org/abs/2508.15128
• Mohammed Elmusrati, 21 Aug 2025, Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI, https://arxiv.org/abs/2508.15719
• Youjia Zhang, Youngeun Kim, Young-Geun Choi, Hongyeob Kim, Huiling Liu, Sungeun Hong, 21 Aug 2025, Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment, https://arxiv.org/abs/2508.15568
• Kanata Oowada and Hideaki Iiduka, 21 Aug 2025, Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size, https://arxiv.org/abs/2501.18164
• Grzegorz Dudek, Witold Orzeszko, Piotr Fiszeder, 21 Aug 2025, Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts, https://arxiv.org/abs/2508.15922
• Boris N. Oreshkin and Shiv Tavker and Dmitry Efimov, 22 Aug 2025, Probabilistic Pretraining for Neural Regression, https://arxiv.org/abs/2508.16355
• Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband, 22 Aug 2025, Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow, https://arxiv.org/abs/2508.16403
• Madhava Gaikwad, Abhishek Gandhi, 22 Aug 2025, ANSC: Probabilistic Capacity Health Scoring for Datacenter-Scale Reliability, https://arxiv.org/abs/2508.16119
• Amirreza Talebi, 24 Aug 2025, Large Language Model-Based Automatic Formulation for Stochastic Optimization Models, https://arxiv.org/abs/2508.17200
• Johannes Schmalz, Felipe Trevizan, 24 Aug 2025, Solving Constrained Stochastic Shortest Path Problems with Scalarisation, https://arxiv.org/abs/2508.17446
• Tianhua Chen, 18 Aug 2025, From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective, https://arxiv.org/abs/2508.16643
• Baozhuo Su, Zhengxian Qu, 22 Aug 2025, Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression, https://arxiv.org/abs/2508.16802
• Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme, 23 Aug 2025, TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression, https://arxiv.org/abs/2508.17056
• Nanfei Jiang, Hoi-To Wai, Mahnoosh Alizadeh, 23 Aug 2025, Stochastic Gradient Descent with Strategic Querying, https://arxiv.org/abs/2508.17144
• Xing Wei, Yuqi Ouyang, 24 Aug 2025, GPG-HT: Generalized Policy Gradient with History-Aware Decision Transformer for Probabilistic Path Planning, https://arxiv.org/abs/2508.17218
• YongKyung Oh, Seungsu Kam, Dong-Young Lim, Sungil Kim, 24 Aug 2025, Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations, https://arxiv.org/abs/2508.17521
• Novin Shahroudi, Viacheslav Komisarenko, Meelis Kull, 25 Aug 2025, Aligning the Evaluation of Probabilistic Predictions with Downstream Value, https://arxiv.org/abs/2508.18251
• Yue-Jane Liu, Malinda Lu, Matthew K. Nock, Yaniv Yacoby, 23 Aug 2025, Neural Stochastic Differential Equations on Compact State-Spaces, https://arxiv.org/abs/2508.17090
• Md Rejwanur Rahman, Adrian Rodriguez-Marek, Nina Stark, Grace Massey, Carl Friedrichs, Kelly M. Dorgan, 25 Aug 2025, Probabilistic Classification of Near-Surface Shallow-Water Sediments using A Portable Free-Fall Penetrometer, https://arxiv.org/abs/2410.00225
• Ding Huang, Jian Huang, Ting Li, and Guohao Shen, 25 Aug 2025, Conditional Stochastic Interpolation for Generative Learning, https://arxiv.org/abs/2312.05579
• Michela Lapenna, Caterina De Bacco, 22 Aug 2025, How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?, https://arxiv.org/abs/2506.11869
• Tristan Luca Saidi, Abigail Hickok, Bastian Rieck, Andrew J. Blumberg, 3 Sep 2025, EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings, https://arxiv.org/abs/2509.03703
• Yang Chen, Xiao Lin, Bo Yan, Libo Zhang, Jiamou Liu, Neset \"Ozkan Tan, Michael Witbrock, 4 Sep 2025, Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables, https://arxiv.org/abs/2509.03845
• Ozgu Goksu and Nicolas Pugeault, 4 Sep 2025, FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity, https://arxiv.org/abs/2509.04107
• Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti, 4 Sep 2025, Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric, https://arxiv.org/abs/2509.04089
• Jung-hun Kim, Min-hwan Oh, 4 Sep 2025, Batched Stochastic Matching Bandits, https://arxiv.org/abs/2509.04194
• Jianhua Liu, Zheng Liu, Yu Xiang, Yanwen Qu, 4 Sep 2025, Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics, https://arxiv.org/abs/2504.08821
• Lucius Bushnaq, Dan Braun, Lee Sharkey, 4 Sep 2025, Stochastic Parameter Decomposition, https://arxiv.org/abs/2506.20790
• Difei Xu, Meng Ding, Zihang Xiang, Jinhui Xu, Di Wang, 4 Sep 2025, Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition, https://arxiv.org/abs/2509.04668
• Benjamin J. Zhang, Siting Liu, Stanley J. Osher, Markos A. Katsoulakis, 5 Sep 2025, Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations, https://arxiv.org/abs/2509.05186
• Wenchuan Mu and Kwan Hui Lim, 26 Aug 2025, Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness, https://arxiv.org/abs/2508.19183
• Rodrigo Carmo Terin, 26 Aug 2025, The GINN framework: a stochastic QED correspondence for stability and chaos in deep neural networks, https://arxiv.org/abs/2508.18948
• Satchit Chatterji and Erman Acar, 26 Aug 2025, Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning, https://arxiv.org/abs/2411.04867
• Adamo Young, Fei Wang, David S Wishart, Bo Wang, Russell Greiner, Hannes R\"ost, 27 Aug 2025, FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction, https://arxiv.org/abs/2404.02360
• Kesav Kaza, Ramachandran Anantharaman and Rahul Meshram, 27 Aug 2025, Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems, https://arxiv.org/abs/2506.22971
• Heng-Sheng Chang, Prashant G. Mehta, 27 Aug 2025, What can we learn from signals and systems in a transformer? Insights for probabilistic modeling and inference architecture, https://arxiv.org/abs/2508.20211
• Neta Shoham and Haim Avron, 28 Aug 2025, Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS, https://arxiv.org/abs/2508.20588
• Ronak Mehta, Mateus Piovezan Otto, Noah Stanis, Azadeh Yazdan-Shahmorad, Zaid Harchaoui, 28 Aug 2025, Supervised Stochastic Gradient Algorithms for Multi-Trial Source Separation, https://arxiv.org/abs/2508.20618
• Xinli Shi, Xingxing Yuan, Longkang Zhu, Guanghui Wen, 28 Aug 2025, A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks, https://arxiv.org/abs/2508.20645
• Lorenzo Busellato, Federico Cunico, Diego Dall'Alba, Marco Emporio, Andrea Giachetti, Riccardo Muradore, Marco Cristani, 28 Aug 2025, Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting, https://arxiv.org/abs/2508.20812
• Xiaoxuan Yang, Syrine Belakaria, Biresh Kumar Joardar, Huanrui Yang, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabarty, Hai Li, 12 Sep 2021, Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise, https://arxiv.org/abs/2109.05437
• Nicholas H. Nelsen, Yunan Yang, 27 Aug 2025, Operator learning meets inverse problems: A probabilistic perspective, https://arxiv.org/abs/2508.20207
• Zhuoyuan Wang, Raffaele Romagnoli, Kamyar Azizzadenesheli, Yorie Nakahira, 27 Aug 2025, Neural Spline Operators for Risk Quantification in Stochastic Systems, https://arxiv.org/abs/2508.20288
• Facheng Yu and Ronak Mehta and Alex Luedtke and Zaid Harchaoui, 28 Aug 2025, Stochastic Gradients under Nuisances, https://arxiv.org/abs/2508.20326
• Quentin Duchemin and Guillaume Obozinski, 28 Aug 2025, Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts, https://arxiv.org/abs/2502.05157
• Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux, 29 Aug 2025, Adaptive Heavy-Tailed Stochastic Gradient Descent, https://arxiv.org/abs/2508.21353
• Bangti Jin, Longjun Wu, 29 Aug 2025, Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks, https://arxiv.org/abs/2508.21571
• Agustinus Kristiadi, 29 Aug 2025, Introduction to the Analysis of Probabilistic Decision-Making Algorithms, https://arxiv.org/abs/2508.21620
• Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema, Sohee Yang, Wai-Chung Kwan, Xuanli He, Wenda Li, Pasquale Minervini, Eleonora Giunchiglia, Shay B. Cohen, 29 Aug 2025, PiCSAR: Probabilistic Confidence Selection And Ranking, https://arxiv.org/abs/2508.21787
• Ricardo Cannizzaro, Michael Groom, Jonathan Routley, Robert Osazuwa Ness, Lars Kunze, 29 Aug 2025, COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty, https://arxiv.org/abs/2403.14488
• Kaito Ariu, Alexandre Proutiere, Se-Young Yun, 29 Aug 2025, Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model, https://arxiv.org/abs/2306.12968
• Michal Bujak and Rafal Kucharski, 29 Aug 2025, Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework, https://arxiv.org/abs/2508.20723
• Madhav Kanda, Shubham Ugare, Sasa Misailovic, 1 Sep 2025, REFINESTAT: Efficient Exploration for Probabilistic Program Synthesis, https://arxiv.org/abs/2509.01082
• Zihao Wang, Yunjie Li, Lingmin Zan, Zheng Gong, Mengtao Zhu, 1 Sep 2025, StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting, https://arxiv.org/abs/2509.01187
• Aryan Amit Barsainyan, Jing Yu Lim, Dianbo Liu, 1 Sep 2025, Toward a Unified Benchmark and Taxonomy of Stochastic Environments, https://arxiv.org/abs/2509.01793
• Tanvir Islam, 2 Sep 2025, VISP: Volatility Informed Stochastic Projection for Adaptive Regularization, https://arxiv.org/abs/2509.01903
• Chih-Yu Lai, Yu-Chien Ning, and Duane S. Boning, 2 Sep 2025, RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting, https://arxiv.org/abs/2509.02341
• Silei Song, Arash Fahim, Michael Mascagni, 29 Aug 2025, WoSNN: Stochastic Solver for PDEs with Machine Learning, https://arxiv.org/abs/2509.00204
• Laurent Condat and Peter Richt\'arik, 31 Aug 2025, Convergence Analysis of the PAGE Stochastic Algorithm for Convex Finite-Sum Optimization, https://arxiv.org/abs/2509.00737
• Chanju Park (Swansea University), Biagio Lucini (Queen Mary University of London), Gert Aarts (Swansea University), 1 Sep 2025, Phase diagram and eigenvalue dynamics of stochastic gradient descent in multilayer neural networks, https://arxiv.org/abs/2509.01349
• Andrea Montanari, 2 Sep 2025, Sampling, Diffusions, and Stochastic Localization, https://arxiv.org/abs/2305.10690
• Sharan Vaswani, Reza Babanezhad, 2 Sep 2025, Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster, https://arxiv.org/abs/2503.00229
• David Leeftink, \c{C}a\u{g}atay Y{\i}ld{\i}z, Steffen Ridderbusch, Max Hinne, Marcel van Gerven, 2 Sep 2025, Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization, https://arxiv.org/abs/2504.02543
• Zaid Harchaoui, Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi, 29 Aug 2025, Stochastic optimization on matrices and a graphon McKean-Vlasov limit, https://arxiv.org/abs/2210.00422
• Zhaosong Lu, Sanyou Mei, Yifeng Xiao, 31 Aug 2025, Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guarantees, https://arxiv.org/abs/2409.09906
• Arya Akhavan, Alexandre B. Tsybakov, 1 Sep 2025, Gradient-free stochastic optimization for additive models, https://arxiv.org/abs/2503.02131
• Lesi Chen, Junru Li, Jingzhao Zhang, 3 Sep 2025, Faster Gradient Methods for Highly-smooth Stochastic Bilevel Optimization, https://arxiv.org/abs/2509.02937
• Stylianos Loukas Vasileiou, William Yeoh, Alessandro Previti, Tran Cao Son, 2 Sep 2025, On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios, https://arxiv.org/abs/2405.19229
• Viet Hoang Pham and Hyo-Sung Ahn, 6 Sep 2025, Distributed Deep Learning using Stochastic Gradient Staleness, https://arxiv.org/abs/2509.05679
• Daksh Mittal, Shunri Zheng, Jing Dong, Hongseok Namkoong, 6 Sep 2025, Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics, https://arxiv.org/abs/2509.05839
• Su Hyeong Lee and Risi Kondor and Richard Ngo, 8 Sep 2025, Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks, https://arxiv.org/abs/2509.06701
• Marzieh Ajirak, Anand Ravishankar, Petar M. Djuric, 7 Sep 2025, Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights, https://arxiv.org/abs/2509.05877
• Diego Sanchez Espinosa, Erik H Thiede, Yunan Yang, 5 Sep 2025, Cryo-EM as a Stochastic Inverse Problem, https://arxiv.org/abs/2509.05541
• Mert Ketenci, I\~nigo Urteaga, Victor Alfonso Rodriguez, No\'emie Elhadad, Adler Perotte, 8 Sep 2025, Probabilistic Shapley Value Modeling and Inference, https://arxiv.org/abs/2402.04211
• Jose Blanchet, Aleksandar Mijatovi\'c, Wenhao Yang, 5 Sep 2025, Limit Theorems for Stochastic Gradient Descent with Infinite Variance, https://arxiv.org/abs/2410.16340
• Kaizheng Wang, 7 Sep 2025, A Minimalist Bayesian Framework for Stochastic Optimization, https://arxiv.org/abs/2509.07030
• Sergio Chibbaro, Cyril Furtlehner, Th\'eo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti, 9 Sep 2025, Building causation links in stochastic nonlinear systems from data, https://arxiv.org/abs/2509.07701
• Xin Jin, Bohan Li, BAAO Xie, Wenyao Zhang, Jinming Liu, Ziqiang Li, Tao Yang, Wenjun Zeng, 9 Sep 2025, Closed-Loop Unsupervised Representation Disentanglement with $\beta$-VAE Distillation and Diffusion Probabilistic Feedback, https://arxiv.org/abs/2402.02346
• Eman Alqudah, Ashfaq Khokhar, 8 Sep 2025, GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC, https://arxiv.org/abs/2506.15011
• Sahil Rajesh Dhayalkar, 12 Sep 2025, Symbolic Feedforward Networks for Probabilistic Finite Automata: Exact Simulation and Learnability, https://arxiv.org/abs/2509.10034
• Klemen Kotar, Wanhee Lee, Rahul Venkatesh, Honglin Chen, Daniel Bear, Jared Watrous, Simon Kim, Khai Loong Aw, Lilian Naing Chen, Stefan Stojanov, Kevin Feigelis, Imran Thobani, Alex Durango, Khaled Jedoui, Atlas Kazemian, and Dan Yamins, 10 Sep 2025, World Modeling with Probabilistic Structure Integration, https://arxiv.org/abs/2509.09737
• Richard Bergna, Sergio Calvo-Ordo\~nez, Felix L. Opolka, Pietro Li\`o, Jose Miguel Hernandez-Lobato, 12 Sep 2025, Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations, https://arxiv.org/abs/2408.16115
• Ira J.S. Shokar, Rich R. Kerswell, Peter H. Haynes, 11 Sep 2025, Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics, https://arxiv.org/abs/2509.09599
• Thorbj{\o}rn Mosekj{\ae}r Iversen, Lars Car{\o}e S{\o}rensen, Simon Faarvang Mathiesen, Henrik Gordon Petersen, 11 Sep 2025, Global Optimization of Stochastic Black-Box Functions with Arbitrary Noise Distributions using Wilson Score Kernel Density Estimation, https://arxiv.org/abs/2509.09238
• Petrus H. Zwart, 18 Sep 2025, Probabilistic Conformal Coverage Guarantees in Small-Data Settings, https://arxiv.org/abs/2509.15349
• Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Pati\~no, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder, 18 Sep 2025, Predicting Language Models' Success at Zero-Shot Probabilistic Prediction, https://arxiv.org/abs/2509.15356
• Rodion Nazarov and Allen Gehret and Robert Shorten and Jakub Marecek, 18 Sep 2025, Stochastic Sample Approximations of (Local) Moduli of Continuity, https://arxiv.org/abs/2509.15368
• Xiaochuan Gong, Jie Hao, Mingrui Liu, 18 Sep 2025, Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization, https://arxiv.org/abs/2509.15399
• Peter Amorese and Morteza Lahijanian, 19 Sep 2025, Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows, https://arxiv.org/abs/2509.15533
• Xinwen Zhang, Yihan Zhang, Hongchang Gao, 19 Sep 2025, Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noises, https://arxiv.org/abs/2509.15543
• Musen Lin, Minghao Liu, Taoran Lu, Lichen Yuan, Yiwei Liu, Haonan Xu, Yu Miao, Yuhao Chao, Zhaojian Li, 19 Sep 2025, GUI-ReWalk: Massive Data Generation for GUI Agent via Stochastic Exploration and Intent-Aware Reasoning, https://arxiv.org/abs/2509.15738
• Graham Clyne, Guillaume Couairon, Guillaume Gastineau, Claire Monteleoni, Anastase Charantonis, 19 Sep 2025, ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching, https://arxiv.org/abs/2509.15942
• Chunna Li, Yiwei Song, Yuanhai Shao, 19 Sep 2025, SETrLUSI: Stochastic Ensemble Multi-Source Transfer Learning Using Statistical Invariant, https://arxiv.org/abs/2509.15593
• Alexandra Carpentier, Christophe Giraud, and Nicolas Verzelen, 19 Sep 2025, Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities, https://arxiv.org/abs/2509.15822
• Yuanyun Hu, Evan Bell, Guijin Wang, Yu Sun, 19 Sep 2025, PRISM: Probabilistic and Robust Inverse Solver with Measurement-Conditioned Diffusion Prior for Blind Inverse Problems, https://arxiv.org/abs/2509.16106
• Tianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang, Dongyeop Kang, 19 Sep 2025, BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation, https://arxiv.org/abs/2405.17764
• Tairan Fu, David Campo-Nazareno, Javier Coronado-Bl\'azquez, Javier Conde, Pedro Reviriego, Fabrizio Lombardi, 16 Sep 2025, Stochastic Streets: A Walk Through Random LLM Address Generation in four European Cities, https://arxiv.org/abs/2509.12914
• Sanyam Jain, Khuram Naveed, Illia Oleksiienko, Alexandros Iosifidis and Ruben Pauwels, 9 Sep 2025, InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation, https://arxiv.org/abs/2509.12239
• Ruimeng Hu, Jihao Long and Haosheng Zhou, 15 Sep 2025, Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures, https://arxiv.org/abs/2509.12484
• Pratik Nag, 16 Sep 2025, Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting, https://arxiv.org/abs/2509.12708
• Alessandro Antonucci, Eric Rossetto, Ivan Duvnjak, 16 Sep 2025, On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Models, https://arxiv.org/abs/2509.13165
• Rafael Zimmer, Oswaldo Luiz do Valle Costa, 15 Sep 2025, Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics, https://arxiv.org/abs/2509.12456
• Saki Hashimoto, Shoichi Hasegawa, Tomochika Ishikawa, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Tadahiro Taniguchi, 16 Sep 2025, Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model, https://arxiv.org/abs/2509.12754
• Sel Ly, Kapil Chauhan, Anshuman Singh, and Hung Dinh Nguyen, 14 Sep 2025, Meta-model Neural Process for Probabilistic Power Flow under Varying N-1 System Topologies, https://arxiv.org/abs/2509.12281
• Ivan Petej, 14 Sep 2025, Protected Probabilistic Classification Library, https://arxiv.org/abs/2509.11267
• Johann Schmidt, Sebastian Stober, 14 Sep 2025, Geometrically Constrained and Token-Based Probabilistic Spatial Transformers, https://arxiv.org/abs/2509.11218
• Navid Hashemi, Samuel Sasaki, Diego Manzanas Lopez, Ipek Oguz, Meiyi Ma, Taylor T. Johnson, 15 Sep 2025, Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network, https://arxiv.org/abs/2509.11838
• Zixi Chen, Yumin Xu, Ruixun Zhang, 14 Sep 2025, Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence, https://arxiv.org/abs/2509.11039
• Jia-Qi Yang, Lei Shi, 14 Sep 2025, Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning, https://arxiv.org/abs/2509.11070
• Xingchen Wang, Feijie Wu, Chenglin Miao, Tianchun Li, Haoyu Hu, Qiming Cao, Jing Gao, Lu Su, 18 Sep 2025, Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking, https://arxiv.org/abs/2509.14603
• Hyungjoon Soh, Junghyo Jo, 18 Sep 2025, Stochastic Clock Attention for Aligning Continuous and Ordered Sequences, https://arxiv.org/abs/2509.14678
• Stelios Zarifis, Ioannis Kordonis, and Petros Maragos, 18 Sep 2025, Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization, https://arxiv.org/abs/2509.14832
• Zhuanghua Liu, Luo Luo, 18 Sep 2025, Stochastic Bilevel Optimization with Heavy-Tailed Noise, https://arxiv.org/abs/2509.14952
• Jean-Fran\c{c}ois Aujol, J\'er\'emie Bigot, Camille Castera, 18 Sep 2025, Stochastic Adaptive Gradient Descent Without Descent, https://arxiv.org/abs/2509.14969
• Lukas Silvester Barth, Paulo von Petersenn, 18 Sep 2025, Probabilistic and nonlinear compressive sensing, https://arxiv.org/abs/2509.15060
• Andrei Chertkov, Artem Basharin, Mikhail Saygin, Evgeny Frolov, Stanislav Straupe, Ivan Oseledets, 18 Sep 2025, Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers, https://arxiv.org/abs/2509.15113
• Chenzhuo Zhao, Ziqian Liu, Xinda Wang, Junting Lu, Chaoyi Ruan, 18 Sep 2025, PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models, https://arxiv.org/abs/2505.16307
• Kristoffer Andersson, Alessandro Gnoatto, Camilo Andr\'es Garc\'ia Trillos, 18 Sep 2025, A deep solver for backward stochastic Volterra integral equations, https://arxiv.org/abs/2505.18297
• Caio de Prospero Iglesias, Kimberly Villalobos Carballo, Dimitris Bertsimas, 9 Sep 2025, Prescribe-then-Select: Adaptive Policy Selection for Contextual Stochastic Optimization, https://arxiv.org/abs/2509.08194
• Sasan Sharifipour, Constantino \'Alvarez Casado, Mohammad Sabokrou, Miguel Bordallo L\'opez, 9 Sep 2025, APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction, https://arxiv.org/abs/2509.08104
• Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa, 10 Sep 2025, kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions, https://arxiv.org/abs/2509.08366
• Luo Luo, Xue Cui, Tingkai Jia, Cheng Chen, 10 Sep 2025, Decentralized Stochastic Nonconvex Optimization under the Relaxed Smoothness, https://arxiv.org/abs/2509.08726
• Sicco Verwer and Christian Hammerschmidt, 10 Sep 2025, FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata, https://arxiv.org/abs/2203.16331
• Pablo Lemos, Sammy Sharief, Nikolay Malkin, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur, Yashar Hezaveh, 10 Sep 2025, PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation, https://arxiv.org/abs/2402.04355
• Marta Grobelna, Jan K\v{r}et\'insk\'y, Maximilian Weininger, 10 Sep 2025, Stopping Criteria for Value Iteration on Concurrent Stochastic Reachability and Safety Games, https://arxiv.org/abs/2505.21087
• Wei Liu, Yangyang Xu, 10 Sep 2025, A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization, https://arxiv.org/abs/2501.19214
• Md Rezwan Jaher, Abul Mukid Mohammad Mukaddes, A. B. M. Abdul Malek, 17 Sep 2025, Deconstructing Intraocular Pressure: A Non-invasive Multi-Stage Probabilistic Inverse Framework, https://arxiv.org/abs/2509.14167
• Johnny R. Zhang (Independent Researcher), Xiaomei Mi (University of Manchester), Gaoyuan Du (Amazon), Qianyi Sun (Microsoft), Shiqi Wang (Meta), Jiaxuan Li (Amazon), Wenhua Zhou (Independent Researcher), 17 Sep 2025, A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training, https://arxiv.org/abs/2509.14216
• Jacob J. W. Bakermans, Pablo Tano, Reidar Riveland, Charles Findling, Alexandre Pouget, 2 Oct 2025, Compositional meta-learning through probabilistic task inference, https://arxiv.org/abs/2510.01858
• Stefano Bruno, Youngsik Hwang, Jaehyeon An, Sotirios Sabanis, Dong-Young Lim, 2 Oct 2025, Flatness-Aware Stochastic Gradient Langevin Dynamics, https://arxiv.org/abs/2510.02174
• Yanwei Jia, Du Ouyang, and Yufei Zhang, 2 Oct 2025, Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning, https://arxiv.org/abs/2503.09981
• Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti, 2 Oct 2025, Beyond Slater's Condition in Online CMDPs with Stochastic and Adversarial Constraints, https://arxiv.org/abs/2509.20114
• Anton Baumann, Rui Li, Marcus Klasson, Santeri Mentu, Shyamgopal Karthik, Zeynep Akata, Arno Solin, Martin Trapp, 2 Oct 2025, Post-hoc Probabilistic Vision-Language Models, https://arxiv.org/abs/2412.06014
• Jonathan Zheng, Sauvik Das, Alan Ritter, Wei Xu, 2 Oct 2025, Probabilistic Reasoning with LLMs for k-anonymity Estimation, https://arxiv.org/abs/2503.09674
• Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal, 14 Oct 2025, One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration, https://arxiv.org/abs/2510.12088
• Xiaohang Tang, Zhuowen Cheng, Satyabrat Kumar, 13 Oct 2025, Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling, https://arxiv.org/abs/2510.11877
• Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber, Wei Biao Wu, 13 Oct 2025, Statistical Guarantees for High-Dimensional Stochastic Gradient Descent, https://arxiv.org/abs/2510.12013
• Yuki Yasuda and Ryo Onishi, 14 Oct 2025, Probabilistic Super-Resolution for Urban Micrometeorology via a Schr\"odinger Bridge, https://arxiv.org/abs/2510.12148
• Bogdan Butyrin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Qi-Man Shao, Zhuo-Song Zhang, 14 Oct 2025, Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation, https://arxiv.org/abs/2510.12375
• Fred Xu, Thomas Markovich, 14 Oct 2025, Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations, https://arxiv.org/abs/2506.06907
• Zhongxuan Liu, Yue Kang, Thomas C. M. Lee, 30 Sep 2025, Lipschitz Bandits with Stochastic Delayed Feedback, https://arxiv.org/abs/2510.00309
• Marawan Gamal Abdel Hameed, Guillaume Rabusseau, 1 Oct 2025, Efficient Probabilistic Tensor Networks, https://arxiv.org/abs/2510.00382
• Viktor Sip, Martin Breyton, Spase Petkoski and Viktor Jirsa, 1 Oct 2025, Dynamical system reconstruction from partial observations using stochastic dynamics, https://arxiv.org/abs/2510.01089
• Julian Lemmel, Manuel Kranzl, Adam Lamine, Philipp Neubauer, Radu Grosu, Sophie A. Neubauer, 1 Oct 2025, TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubes, https://arxiv.org/abs/2510.00906
• Vikram Krishnamurthy and Luke Snow, 30 Sep 2025, Malliavin Calculus with Weak Derivatives for Counterfactual Stochastic Optimization, https://arxiv.org/abs/2510.00297
• Leah Bar, Liron Mor Yosef, Shai Zucker, Neta Shoham, Inbar Seroussi, Nir Sochen, 1 Oct 2025, A Geometric Unification of Generative AI with Manifold-Probabilistic Projection Models, https://arxiv.org/abs/2510.00666
• Nurbek Tastan, Samuel Horvath, Karthik Nandakumar, 1 Oct 2025, Stochastic Self-Organization in Multi-Agent Systems, https://arxiv.org/abs/2510.00685
• Yan Chen, Tao Li and Xiaofeng Zong, 1 Oct 2025, Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization, https://arxiv.org/abs/2407.02765
• Dingling Yao, Filip Tronarp, Nathanael Bosch, 1 Oct 2025, Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers, https://arxiv.org/abs/2503.04684
• Andreas Lebedev, Abhinav Das, Sven Pappert, Stephan Schl\"uter, 23 Sep 2025, Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting, https://arxiv.org/abs/2509.19417
• Birk Torpmann-Hagen, P{\aa}l Halvorsen, Michael A. Riegler, Dag Johansen, 23 Sep 2025, Probabilistic Runtime Verification, Evaluation and Risk Assessment of Visual Deep Learning Systems, https://arxiv.org/abs/2509.19419
• Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu, 24 Sep 2025, From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting, https://arxiv.org/abs/2509.19975
• Li Zhou, Elvan Ceyhan, 23 Sep 2025, Stochastic Path Planning in Correlated Obstacle Fields, https://arxiv.org/abs/2509.19559
• Andrea Della Vecchia, Damir Filipovi\'c, 24 Sep 2025, Error Propagation in Dynamic Programming: From Stochastic Control to Option Pricing, https://arxiv.org/abs/2509.20239
• Keisuke Kamo and Hideaki Iiduka, 24 Sep 2025, Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum, https://arxiv.org/abs/2501.08883
• Wenpin Tang and Fuzhong Zhou, 23 Sep 2025, Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond, https://arxiv.org/abs/2403.06279
• Andra\v{z} Jelin\v{c}i\v{c}, Owen Lockwood, Akhil Garlapati, Guillaume Verdon, Trevor McCourt, 28 Oct 2025, An efficient probabilistic hardware architecture for diffusion-like models, https://arxiv.org/abs/2510.23972
• Yohan Abeysinghe, Muhammad Akhtar Munir, Sanoojan Baliah, Ron Sarafian, Fahad Shahbaz Khan, Yinon Rudich, Salman Khan, 28 Oct 2025, Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling, https://arxiv.org/abs/2510.23977
• Samuel G. Fadel, Hrittik Roy, Nicholas Kr\"amer, Yevgen Zainchkovskyy, Stas Syrota, Alejandro Valverde Mahou, Carl Henrik Ek, S{\o}ren Hauberg, 27 Oct 2025, VIKING: Deep variational inference with stochastic projections, https://arxiv.org/abs/2510.23684
• Madhurima Panja, Dhiman Das, Tanujit Chakraborty, Arnob Ray, R. Athulya, Chittaranjan Hens, Syamal K. Dana, Nuncio Murukesh, Dibakar Ghosh, 28 Oct 2025, Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers, https://arxiv.org/abs/2510.24254
• Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani, 27 Oct 2025, Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, https://arxiv.org/abs/2004.06231
• Khoa Nguyen, Khang Tran, NhatHai Phan, Cristian Borcea, Rouming Jin, Issa Khalil, 28 Oct 2025, SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning, https://arxiv.org/abs/2510.23455
• Tito Homem-de-Mello, Juan Valencia, Felipe Lagos, Guido Lagos, 28 Oct 2025, Forecasting Outside the Box: Application-Driven Optimal Pointwise Forecasts for Stochastic Optimization, https://arxiv.org/abs/2411.03520
• Xiaoming Wu, Teng Liu, Xin Wang, Ming Yang and Jiguo Yu, 23 Oct 2025, ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push, https://arxiv.org/abs/2510.20157
• Spencer Young, Riley Sinema, Cole Edgren, Andrew Hall, Nathan Dong, Porter Jenkins, 22 Oct 2025, Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence, https://arxiv.org/abs/2405.12412
• Patrick Cheridito, Jean-Loup Dupret, Donatien Hainaut, 23 Oct 2025, Deep Learning for Continuous-time Stochastic Control with Jumps, https://arxiv.org/abs/2505.15602
• Manh Cuong Dao, The Hung Tran, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang, 23 Oct 2025, ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge, https://arxiv.org/abs/2509.16300
• G\'erard Ben Arous, C\'edric Gerbelot, Vanessa Piccolo, 23 Oct 2025, Stochastic gradient descent in high dimensions for multi-spiked tensor PCA, https://arxiv.org/abs/2410.18162
• Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson, Mathieu Blanchette, Alexander Tong, Stephen Y. Zhang, Lazar Atanackovic, 18 Oct 2025, Simulation-free Structure Learning for Stochastic Dynamics, https://arxiv.org/abs/2510.16656
• Shurong Lin, Eric D. Kolaczyk, Adam Smith, Elliot Paquette, 19 Oct 2025, High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares, https://arxiv.org/abs/2510.16687
• Viktoria Schram, Markus Hiller, Daniel Beck, Trevor Cohn, 19 Oct 2025, Zero-Shot Performance Prediction for Probabilistic Scaling Laws, https://arxiv.org/abs/2510.16743
• Cristian J. Vaca-Rubio, Roberto Pereira, Luis Blanco, Engin Zeydan, M\`arius Caus, 19 Oct 2025, A Primer on Kolmogorov-Arnold Networks (KANs) for Probabilistic Time Series Forecasting, https://arxiv.org/abs/2510.16940
• Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti, Taira Tsuchiya, 20 Oct 2025, Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback, https://arxiv.org/abs/2510.17103
• Hequn Li and Zhongwei Deng and Chunlin Jiang and Yvxin He andZhansheng Ning, 20 Oct 2025, A Conditional Diffusion Model for Probabilistic Prediction of Battery Capacity Degradation, https://arxiv.org/abs/2510.17414
• El Mahdi Chayti and Martin Jaggi, 20 Oct 2025, Stochastic Difference-of-Convex Optimization with Momentum, https://arxiv.org/abs/2510.17503
• Yang Li, Zhi Chen, 30 Aug 2025, FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance, https://arxiv.org/abs/2510.15883
• Wang-Ji Yan (1 and 2), Lin-Feng Mei (1), Jiang Mo (1), Costas Papadimitriou (3), Ka-Veng Yuen (1 and 2), and Michael Beer (4,5, and 6) ((1) State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, (2) Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, China, (3) Department of Mechanical Engineering, University of Thessaly, (4) Leibniz University Hannover, Institute for Risk and Reliability, (5) Department of Civil and Environmental Engineering, University of Liverpool, (6) International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University), 19 Oct 2025, Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems, https://arxiv.org/abs/2408.08629
• Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld, 20 Oct 2025, Deep learning based numerical approximation algorithms for stochastic partial differential equations, https://arxiv.org/abs/2012.01194
• Zhangyong Liang, Ji Zhang, Xin Wang, Pengfei Zhang, Zhao Li, 20 Oct 2025, Neural Dynamic Data Valuation: A Stochastic Optimal Control Approach, https://arxiv.org/abs/2404.19557
• Dipayan Sanpui, Anirban Chandra, Henry Chan, Sukriti Manna, Subramanian KRS Sankaranarayanan, 15 Sep 2025, Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing, https://arxiv.org/abs/2509.16233
• Tian Xie, Ding Zhu, Jia Liu, Mahdi Khalili, Xueru Zhang, 22 Sep 2025, SPRINT: Stochastic Performative Prediction With Variance Reduction, https://arxiv.org/abs/2509.17304
• Haocheng Luo, Mehrtash Harandi, Dinh Phung, Trung Le, 22 Sep 2025, Unveiling m-Sharpness Through the Structure of Stochastic Gradient Noise, https://arxiv.org/abs/2509.18001
• Runjia Zeng, James Chenhao Liang, Cheng Han, Zhiwen Cao, Jiahao Liu, Xiaojun Quan, Yingjie Victor Chen, Lifu Huang, Tong Geng, Qifan Wang, Dongfang Liu, 21 Sep 2025, Probabilistic Token Alignment for Large Language Model Fusion, https://arxiv.org/abs/2509.17276
• Manish Prajapat, Johannes K\"ohler, Melanie N. Zeilinger, Andreas Krause, 20 Sep 2025, Safe Guaranteed Dynamics Exploration with Probabilistic Models, https://arxiv.org/abs/2509.16650
• Divyesh Savaliya and Marius E. Yamakou, 26 Oct 2025, Self-induced stochastic resonance: A physics-informed machine learning approach, https://arxiv.org/abs/2510.22848
• Lingxiao Huang, Zhize Li, Nisheeth K. Vishnoi, Runkai Yang, Haoyu Zhao, 27 Oct 2025, Coresets for Clustering Under Stochastic Noise, https://arxiv.org/abs/2510.23438
• Austin A. Barr, Brij S. Karmur, Anthony J. Winder, Eddie Guo, John T. Lysack, James N. Scott, William F. Morrish, Muneer Eesa, Morgan Willson, David W. Cadotte, Michael M.H. Yang, Ian Y.M. Chan, Sanju Lama, Garnette R. Sutherland, 25 Oct 2025, Expert Validation of Synthetic Cervical Spine Radiographs Generated with a Denoising Diffusion Probabilistic Model, https://arxiv.org/abs/2510.22166
• Giora Simchoni, Saharon Rosset, 25 Oct 2025, MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models, https://arxiv.org/abs/2510.22198
• Swagatam Das, 26 Oct 2025, A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes, https://arxiv.org/abs/2510.22778
• Milad Sefidgaran, Kimia Nadjahi, Abdellatif Zaidi, 27 Oct 2025, Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization, https://arxiv.org/abs/2510.23485
• John Leland, YooJung Choi, 24 Oct 2025, On the Hardness of Approximating Distributions with Tractable Probabilistic Models, https://arxiv.org/abs/2506.01281
• Mert Gurbuzbalaban, Mohammad Rafiqul Islam, Xiaoyu Wang, Lingjiong Zhu, 26 Oct 2025, Generalized EXTRA stochastic gradient Langevin dynamics, https://arxiv.org/abs/2412.01993
• Haruki Settai, Naoya Takeishi, and Takehisa Yairi, 26 Oct 2025, A Temporal Difference Method for Stochastic Continuous Dynamics, https://arxiv.org/abs/2505.15544
• Ning Zhang, Henry Kenlay, Li Zhang, Mihai Cucuringu, Xiaowen Dong, 27 Oct 2025, On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective, https://arxiv.org/abs/2506.01213
• Mara Daniels, 25 Oct 2025, On the Contractivity of Stochastic Interpolation Flow, https://arxiv.org/abs/2504.10653
• Naomi Desobry, Elnura Zhalieva, Souhaib Ben Taieb, 27 Oct 2025, Enforcing Calibration in Multi-Output Probabilistic Regression with Pre-rank Regularization, https://arxiv.org/abs/2510.21273
• Boyou Chen, Gerui Xu, Zifei Wang, Huizhong Guo, Ananna Ahmed, Zhaonan Sun, Zhen Hu, Kaihan Zhang, Shan Bao, 14 Oct 2025, From Narratives to Probabilistic Reasoning: Predicting and Interpreting Drivers' Hazardous Actions in Crashes Using Large Language Model, https://arxiv.org/abs/2510.13002
• Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann, 15 Oct 2025, UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations, https://arxiv.org/abs/2510.13774
• Belinda Trotta, Robert Johnson, Catherine de Burgh-Day, Debra Hudson, Esteban Abellan, James Canvin, Andrew Kelly, Daniel Mentiplay, Benjamin Owen, Jennifer Whelan, 15 Oct 2025, Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models, https://arxiv.org/abs/2504.12672
• Yukin Zhang, Qi Dong, 15 Oct 2025, Multi-Scale Probabilistic Generation Theory: A Unified Information-Theoretic Framework for Hierarchical Structure in Large Language Models, https://arxiv.org/abs/2505.18244
• Parikshit Pareek, Sidhant Misra and Deepjyoti Deka, 15 Oct 2025, Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk, https://arxiv.org/abs/2308.07867
• Anna Kalenkova, Lu Xia, Dirk Neumann, 16 Aug 2025, Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail, https://arxiv.org/abs/2509.21322
• Yunchen Li, Shaohui Lin, Zhou Yu, 26 Sep 2025, Generation Properties of Stochastic Interpolation under Finite Training Set, https://arxiv.org/abs/2509.21925
• Maria Lomeli and Matthijs Douze and Gergely Szilvasy and Loic Cabannes and Jade Copet and Sainbayar Sukhbaatar and Jason Weston and Gabriel Synnaeve and Pierre-Emmanuel Mazar\'e and Herv\'e J\'egou, 26 Sep 2025, Stochastic activations, https://arxiv.org/abs/2509.22358
• Luca Callisti, Marco Romito, Francesco Triggiano, 25 Sep 2025, Effective continuous equations for adaptive SGD: a stochastic analysis view, https://arxiv.org/abs/2509.21614
• Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok Veeraraghavan, Guha Balakrishnan, 26 Sep 2025, Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction, https://arxiv.org/abs/2404.15274
• Subed Lamichhane, Haotian Lu, Sheldon X.-D. Tan, 26 Sep 2025, BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase, https://arxiv.org/abs/2503.17393
• Ziwei Luo, Fredrik K. Gustafsson, Jens Sj\"olund, Thomas B. Sch\"on, 26 Sep 2025, Forward-only Diffusion Probabilistic Models, https://arxiv.org/abs/2505.16733
• Yiyang Zhang, Junyi Liu, Xiaobo Zhao, 26 Sep 2025, Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information, https://arxiv.org/abs/2304.13646
• Matteo Lapucci and Davide Pucci, 26 Sep 2025, Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems, https://arxiv.org/abs/2411.07102
• Akash Yadav and Ruda Zhang, 7 Oct 2025, Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models, https://arxiv.org/abs/2510.06439
• Shiye Su, Yuhui Zhang, Linqi Zhou, Rajesh Ranganath, Serena Yeung-Levy, 8 Oct 2025, Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling, https://arxiv.org/abs/2510.06634
• Zhuoyuan Wang, Albert Chern, Yorie Nakahira, 7 Oct 2025, Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems, https://arxiv.org/abs/2407.08868
• Le-Tuyet-Nhi Pham and Dario Shariatian and Antonio Ocello and Giovanni Conforti and Alain Durmus, 8 Oct 2025, Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions, https://arxiv.org/abs/2502.07939
• Dong Lao, Yuxiang Zhang, Haniyeh Ehsani Oskouie, Yangchao Wu, Alex Wong, Stefano Soatto, 3 Oct 2025, Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles, https://arxiv.org/abs/2510.03224
• Yuping Zheng, Andrew Lamperski, 3 Oct 2025, Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential, https://arxiv.org/abs/2510.02735
• Jiaming Liang, 3 Oct 2025, Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method, https://arxiv.org/abs/2402.08992
• Feifan Xia, Yuyang Fang, Defang Li, Yantong Xie, Weikang Li, Yang Li, Deguo Xia, Jizhou Huang, 21 Oct 2025, Probabilistic Modeling of Intentions in Socially Intelligent LLM Agents, https://arxiv.org/abs/2510.18476
• Ziyu Lu, Anna J. Li, Alexander E. Ladd, Pascha Matveev, Aditya Deole, Eric Shea-Brown, J. Nathan Kutz, Nicholas A. Steinmetz, 20 Oct 2025, Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity, https://arxiv.org/abs/2510.18037
• Weijie Xia, Chenguang Wang, Peter Palensky, Pedro P. Vergara, 21 Oct 2025, A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction, https://arxiv.org/abs/2405.02180
• Patrick Seifner and Kostadin Cvejoski and David Berghaus and Cesar Ojeda and Ramses J. Sanchez, 21 Oct 2025, In-Context Learning of Stochastic Differential Equations with Foundation Inference Models, https://arxiv.org/abs/2502.19049
• Edwin Hamel-De le Court and Gaspard Ohlmann and Francesco Belardinelli, 21 Oct 2025, ProSh: Probabilistic Shielding for Model-free Reinforcement Learning, https://arxiv.org/abs/2510.15720
• Weixin Chen, Han Zhao, 24 Sep 2025, Understanding and Improving Adversarial Robustness of Neural Probabilistic Circuits, https://arxiv.org/abs/2509.20549
• Andrii Kliachkin, Jana Lep\v{s}ov\'a, Gilles Bareilles, Jakub Mare\v{c}ek, 25 Sep 2025, humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems, https://arxiv.org/abs/2509.21254
• Yanfeng Yang, Siwei Chen, Pingping Hu, Zhaotong Shen, Yingjie Zhang, Zhuoran Sun, Shuai Li, Ziqi Chen, Kenji Fukumizu, 25 Sep 2025, Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting, https://arxiv.org/abs/2509.20928
• Karlo Koledi\'c, Luka Petrovi\'c, Ivan Markovi\'c, Ivan Petrovi\'c, 25 Sep 2025, GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion, https://arxiv.org/abs/2412.06080
• Arthur S. Bianchessi, Yasmin C. Aguirre, Rodrigo C. Barros, Lucas S. Kupssinsk\"u, 25 Sep 2025, Bayesian Attention Mechanism: A Probabilistic Framework for Positional Encoding and Context Length Extrapolation, https://arxiv.org/abs/2505.22842
• David P. Morton, Oscar Dowson, Bernardo K. Pagnoncelli, 26 Sep 2025, MDP modeling for multi-stage stochastic programs, https://arxiv.org/abs/2509.22981
• Ashkan Shahbazi, Chayne Thrash, Yikun Bai, Keaton Hamm, Navid NaderiAlizadeh, Soheil Kolouri, 27 Sep 2025, LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport, https://arxiv.org/abs/2509.23436
• Jianxin Zhang, Clayton Scott, 29 Sep 2025, Semantic Editing with Coupled Stochastic Differential Equations, https://arxiv.org/abs/2509.24223
• Egor Gladin, Alexey Kroshnin, Jia-Jie Zhu, Pavel Dvurechensky, 29 Sep 2025, Improved Stochastic Optimization of LogSumExp, https://arxiv.org/abs/2509.24894
• Jake R. Watts, Joel Sokol, 28 Sep 2025, A Voter-Based Stochastic Rejection-Method Framework for Asymptotically Safe Language Model Outputs, https://arxiv.org/abs/2407.16994
• Zhi Sheng, Yuan Yuan, Yudi Zhang, Jingtao Ding, Yong Li, 27 Sep 2025, Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems, https://arxiv.org/abs/2502.11013
• Siddharth Chandak, Shaan Ul Haque, Nicholas Bambos, 28 Sep 2025, Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise, https://arxiv.org/abs/2503.18391
• Aaron Zweig, Zaikang Lin, Elham Azizi, David Knowles, 29 Sep 2025, Towards Identifiability of Interventional Stochastic Differential Equations, https://arxiv.org/abs/2505.15987
• Linli Zhou, Bokun Wang, My T. Thai, Tianbao Yang, 28 Sep 2025, Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization, https://arxiv.org/abs/2505.21944
• Weiqiu You, Anton Xue, Shreya Havaldar, Delip Rao, Helen Jin, Chris Callison-Burch, Eric Wong, 28 Sep 2025, Probabilistic Soundness Guarantees in LLM Reasoning Chains, https://arxiv.org/abs/2507.12948
• Runyao Yu, Yuchen Tao, Fabian Leimgruber, Tara Esterl, Jochen Stiasny, Qingsong Wen, Hongye Guo, Jochen L. Cremer, 28 Sep 2025, OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting, https://arxiv.org/abs/2502.06830
• Mumuksh Tayal, Manan Tayal, Ravi Prakash, 27 Sep 2025, RISE: Robust Imitation through Stochastic Encoding, https://arxiv.org/abs/2503.12243
• Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton, 29 Sep 2025, Network inference via process motifs for lagged correlation in linear stochastic processes, https://arxiv.org/abs/2208.08871
• Luyao Guo, Luqing Wang, Xinli Shi, Jinde Cao, 27 Sep 2025, A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization, https://arxiv.org/abs/2312.11861
• Tingting Ni, Maryam Kamgarpour, 27 Sep 2025, A learning-based approach to stochastic optimal control under reach-avoid constraint, https://arxiv.org/abs/2412.16561
• Siddharth Chandak, 29 Sep 2025, Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation: Finite-Time Analysis, https://arxiv.org/abs/2501.10806
• Gwen Yidou Weng, Benjie Wang, Guy Van den Broeck, 29 Sep 2025, TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation, https://arxiv.org/abs/2504.18535
• Marcel Hudiani, 29 Sep 2025, Almost Sure Convergence for the Last Iterate of Stochastic Gradient Descent Schemes, https://arxiv.org/abs/2507.07281
• Lino Gerlach, Liv V{\aa}ge, Thore Gerlach, Elliott Kauffman, 17 Oct 2025, WARP-LUTs - Walsh-Assisted Relaxation for Probabilistic Look Up Tables, https://arxiv.org/abs/2510.15655
• El Mahdi Chayti and Taha El Bakkali El Kadi and Omar Saadi and Martin Jaggi, 17 Oct 2025, Stochastic Optimization with Random Search, https://arxiv.org/abs/2510.15610
• Anna C.M. Th\"oni, Yoram Bachrach and Tal Kachman, 17 Oct 2025, Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations, https://arxiv.org/abs/2504.13228
• Jinhui Bai, Andreas Christmann and Lei Shi, 5 Oct 2025, Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions, https://arxiv.org/abs/2510.04237
• Jinyang Jiang, Bernd Heidergott, Jiaqiao Hu, Yijie Peng, 6 Oct 2025, Stochastic Approximation Methods for Distortion Risk Measure Optimization, https://arxiv.org/abs/2510.04563
• Carlo Kneissl, Christopher B\"ulte, Philipp Scholl, Gitta Kutyniok, 6 Oct 2025, Improved probabilistic regression using diffusion models, https://arxiv.org/abs/2510.04583
• Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu, 6 Oct 2025, Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective, https://arxiv.org/abs/2510.05023
• Wenchao He, Tao Jia, 6 Oct 2025, Deep learning framework for predicting stochastic take-off and die-out of early spreading, https://arxiv.org/abs/2510.04574
• Yue wu, 4 Oct 2025, A Trustworthy Industrial Fault Diagnosis Architecture Integrating Probabilistic Models and Large Language Models, https://arxiv.org/abs/2510.03815
• Ziyi Chen, Peiran Yu, Heng Huang, 6 Oct 2025, Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization, https://arxiv.org/abs/2510.04446
• Bobby Shi, Kevin Tian, Matthew S. Zhang, 6 Oct 2025, Perspectives on Stochastic Localization, https://arxiv.org/abs/2510.04460
• Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura, 6 Oct 2025, Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules, https://arxiv.org/abs/2409.16238
• Charikleia Iakovidou, Kibaek Kim, 6 Oct 2025, Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays, https://arxiv.org/abs/2405.10123
• Anji Liu, Zilei Shao, Guy Van den Broeck, 3 Oct 2025, Rethinking Probabilistic Circuit Parameter Learning, https://arxiv.org/abs/2505.19982
• Sanghyuk Chun and Wonjae Kim and Song Park and Sangdoo Yun, 5 Oct 2025, Probabilistic Language-Image Pre-Training, https://arxiv.org/abs/2410.18857
• Chuanqing Pu, Feilong Fan, Nengling Tai, Songyuan Liu, Jinming Yu, 4 Oct 2025, A Hybrid Strategy for Probabilistic Forecasting and Trading of Aggregated Wind-Solar Power: Design and Analysis in HEFTCom2024, https://arxiv.org/abs/2505.10367
• Jingxuan Wu, Zhenglin Wan, Xingrui Yu, Yuzhe Yang, Bo An, Ivor Tsang, 10 Oct 2025, OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching, https://arxiv.org/abs/2510.09060
• Aniss Aiman Medbouhi, Alejandro Garc\'ia-Castellanos, Giovanni Luca Marchetti, Daniel Pelt, Erik J Bekkers, Danica Kragic, 10 Oct 2025, Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree, https://arxiv.org/abs/2510.09328
• Sankalp Gilda, 10 Oct 2025, deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning, https://arxiv.org/abs/2510.09362
• Conor Hassan, Nasrulloh Loka, Cen-You Li, Daolang Huang, Paul E. Chang, Yang Yang, Francesco Silvestrin, Samuel Kaski, Luigi Acerbi, 10 Oct 2025, Efficient Autoregressive Inference for Transformer Probabilistic Models, https://arxiv.org/abs/2510.09477
• Cheng Yin, Yi Chen, 10 Oct 2025, A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems, https://arxiv.org/abs/2311.04014
• Kaiyan Zhao, Tsuguchika Tabaru, Kenichi Kobayashi, Takumi Honda, Masafumi Yamazaki, Yoshimasa Tsuruoka, 10 Oct 2025, Direct Quantized Training of Language Models with Stochastic Rounding, https://arxiv.org/abs/2412.04787
• Abdelrahman S. Abdelrahman, Shuvro Chowdhury, Flaviano Morone, and Kerem Y. Camsari, 10 Oct 2025, Generalized Probabilistic Approximate Optimization Algorithm, https://arxiv.org/abs/2507.07420
• Stephen Zhao, Aidan Li, Rob Brekelmans, Roger Grosse, 24 Oct 2025, Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference, https://arxiv.org/abs/2510.21184
• Noah Oberweis, Semih Cayci, 24 Oct 2025, Convergence of Stochastic Gradient Langevin Dynamics in the Lazy Training Regime, https://arxiv.org/abs/2510.21245
• Tobias Fuchs, Nadja Klein, 24 Oct 2025, Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation, https://arxiv.org/abs/2510.21300
• Emre Sahinoglu, Youbang Sun, Shahin Shahrampour, 24 Oct 2025, Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds, https://arxiv.org/abs/2510.21468
• Zhichao Zhu, Yang Qi, Hengyuan Ma, Wenlian Lu, Jianfeng Feng, 24 Oct 2025, Stochastic Forward-Forward Learning through Representational Dimensionality Compression, https://arxiv.org/abs/2505.16649
• Minchan Jeong, J. Jon Ryu, Se-Young Yun, Gregory W. Wornell, 24 Oct 2025, Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems, https://arxiv.org/abs/2507.07222
• David Simchi-Levi, Zeyu Zheng, Feng Zhu, 24 Oct 2025, Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk, https://arxiv.org/abs/2304.04341
• Jaihoon Kim, Taehoon Yoon, Jisung Hwang, Minhyuk Sung, 24 Oct 2025, Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing, https://arxiv.org/abs/2503.19385
• Parsa Gooya, Reinel Sospedra-Alfonso, 10 Oct 2025, Probabilistic bias adjustment of seasonal predictions of Arctic Sea Ice Concentration, https://arxiv.org/abs/2510.09891
• Shivani Shukla and Himanshu Joshi, 12 Oct 2025, A Stochastic Differential Equation Framework for Multi-Objective LLM Interactions: Dynamical Systems Analysis with Code Generation Applications, https://arxiv.org/abs/2510.10739
• Hanchang Cheng, Weimin Mu, Fan Liu, Weilin Zhu, Can Ma, 13 Oct 2025, LPCVAE: A Conditional VAE with Long-Term Dependency and Probabilistic Time-Frequency Fusion for Time Series Anomaly Detection, https://arxiv.org/abs/2510.10915
• Xuan Tang, Han Zhang, Yuan Cao, Difan Zou, 13 Oct 2025, Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks, https://arxiv.org/abs/2510.11354
• Zihao Zhao, Christopher Yeh, Lingkai Kong, Kai Wang, 13 Oct 2025, Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization, https://arxiv.org/abs/2510.11590
• KiHyun Nam, Jongmin Choi, Hyeongkeun Lee, Jungwoo Heo, Joon Son Chung, 13 Oct 2025, Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap, https://arxiv.org/abs/2510.11330
• Chuan He, Zhaosong Lu, 13 Oct 2025, Accelerated stochastic first-order method for convex optimization under heavy-tailed noise, https://arxiv.org/abs/2510.11676
• Nicholas Pischke, 12 Oct 2025, Mean-square and linear convergence of a stochastic proximal point algorithm in metric spaces of nonpositive curvature, https://arxiv.org/abs/2510.10697
• Yaozhong Shi and Zachary E. Ross and Domniki Asimaki and Kamyar Azizzadenesheli, 11 Oct 2025, Stochastic Process Learning via Operator Flow Matching, https://arxiv.org/abs/2501.04126
• Han Zhang, Yunjiang Jiang, Mingming Li, Haowei Yuan, Yiming Qiu, Wen-Yun Yang, 11 Oct 2025, pEBR: A Probabilistic Approach to Embedding Based Retrieval, https://arxiv.org/abs/2410.19349
• Wiktor Jan Hoffmann, Sonia Laguna, Moritz Vandenhirtz, Emanuele Palumbo, Julia E. Vogt, 9 Oct 2025, Post-hoc Stochastic Concept Bottleneck Models, https://arxiv.org/abs/2510.08219
• Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu, 9 Oct 2025, Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning, https://arxiv.org/abs/2510.08226
• Sanghwa Kim (KAIST), Dohyun Ahn (The Chinese University of Hong Kong), Seungki Min (Seoul National University), 9 Oct 2025, On the Optimality of Tracking Fisher Information in Adaptive Testing with Stochastic Binary Responses, https://arxiv.org/abs/2510.07862
• Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, 9 Oct 2025, Stochastic Interpolants: A Unifying Framework for Flows and Diffusions, https://arxiv.org/abs/2303.08797
• Nachiket N. Naik, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar and Sreedath Panat, 9 Sep 2025, BULL-ODE: Bullwhip Learning with Neural ODEs and Universal Differential Equations under Stochastic Demand, https://arxiv.org/abs/2509.18105
• Han-Lin Hsieh, Maryam M. Shanechi, 22 Sep 2025, Probabilistic Geometric Principal Component Analysis with application to neural data, https://arxiv.org/abs/2509.18469
• Arman Mohammadi, Mattias Krysander, Daniel Jung, Erik Frisk, 23 Sep 2025, Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems, https://arxiv.org/abs/2509.18810
• Asela Hevapathige, 23 Sep 2025, Graph Neural Networks with Similarity-Navigated Probabilistic Feature Copying, https://arxiv.org/abs/2509.19084
• Amirhesam Aghanouri, Cristina Olaverri-Monreal, 23 Sep 2025, LiDAR Point Cloud Image-based Generation Using Denoising Diffusion Probabilistic Models, https://arxiv.org/abs/2509.18917
• Jonathan Schmidt, Luca Schmidt, Felix Strnad, Nicole Ludwig, Philipp Hennig, 23 Sep 2025, A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation, https://arxiv.org/abs/2412.15361
• Nivar Anwer (Institute of Artificial Intelligence, De Montfort University, Leicester, United Kingdom), 22 Oct 2025, Universal Quantitative Abstraction: Categorical Duality and Logical Completeness for Probabilistic Systems, https://arxiv.org/abs/2510.19444
• Archer Dodson, Ritabrata Dutta, 21 Oct 2025, Signature Kernel Scoring Rule as Spatio-Temporal Diagnostic for Probabilistic Forecasting, https://arxiv.org/abs/2510.19110
• Yutong Liang, Chang Hou, Guy Y. Cornejo Maceda, Andrea Ianiro, Stefano Discetti, Andrea Meil\'an-Vila, Didier Sornette, Sandro Claudio Lera, Jialong Chen, Xiaozhou He, and Bernd R. Noack, 24 Sep 2025, Sensor optimization for urban wind estimation with cluster-based probabilistic framework, https://arxiv.org/abs/2509.25222
• Fangji Wang, Panagiotis Tsiotras, 30 Sep 2025, BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks, https://arxiv.org/abs/2509.25647
• Zihui Zhao, Yuanbo Tang, Jieyu Ren, Xiaoping Zhang, Yang Li, 30 Sep 2025, A Unified Probabilistic Framework for Dictionary Learning with Parsimonious Activation, https://arxiv.org/abs/2509.25690
• Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman, 30 Sep 2025, A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise, https://arxiv.org/abs/2509.25730
• Anthony Zhou, Alexander Wikner, Amaury Lancelin, Pedram Hassanzadeh, Amir Barati Farimani, 30 Sep 2025, Reframing Generative Models for Physical Systems using Stochastic Interpolants, https://arxiv.org/abs/2509.26282
• Kirill Tamogashev and Nikolay Malkin, 30 Sep 2025, Data-to-Energy Stochastic Dynamics, https://arxiv.org/abs/2509.26364
• Bojian Yin, Federico Corradi, 30 Sep 2025, Stochastic Layer-wise Learning: Scalable and Efficient Alternative to Backpropagation, https://arxiv.org/abs/2505.05181
• Simon Segert and Nathan Wycoff, 6 Oct 2025, A Probabilistic Basis for Low-Rank Matrix Learning, https://arxiv.org/abs/2510.05447
• Minoh Jeong, Seonho Kim, Alfred Hero, 6 Oct 2025, Probabilistic Variational Contrastive Learning, https://arxiv.org/abs/2506.10159
• Linlu Qiu, Fei Sha, Kelsey Allen, Yoon Kim, Tal Linzen, Sjoerd van Steenkiste, 7 Oct 2025, Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models, https://arxiv.org/abs/2503.17523
• Jan Corazza, Ivan Gavran, Daniel Neider, 16 Oct 2025, Reinforcement Learning with Stochastic Reward Machines, https://arxiv.org/abs/2510.14837
• Elizabeth Collins-Woodfin and Inbar Seroussi, 15 Oct 2025, Exact Dynamics of Multi-class Stochastic Gradient Descent, https://arxiv.org/abs/2510.14074
• Rishal Aggarwal, Jacky Chen, Nicholas M. Boffi, David Ryan Koes, 15 Oct 2025, BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation, https://arxiv.org/abs/2507.00846

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