Stochastic Approximation

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14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

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  1. Scalar-Stepsize Nonuniform Monte Carlo Optimistic Policy Iteration: A Certified Counterexample

    Jun 14, 2026Yuanlong ChenMarkov Decision ProcessesPolicy Optimization

  2. Nonlinear Two-Time-Scale Stochastic Approximation: A Sharp Phase Transition and How to Beat It

    Jun 12, 2026Dhruv Sarkar, Vaneet AggarwalStochastic ApproximationTwo-Timescale Stochastic Approximation

    1Indian Institute of Technology Kharagpur · 2Mohamed bin Zayed University of Artificial Intelligence · 3Purdue University

  3. Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples

    Jun 4, 2026Ziad Kobeissi, Éloïse BerthierStochastic ApproximationTemporal-Difference Learning

    L2S · L2S, INRIA Université Paris-Saclay, CentraleSupelec, Gif-sur-Yvette, France · U2IS +1

  4. Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

    Jun 1, 2026Ahanaf Hasan AriqStochastic ApproximationTwo-Timescale Stochastic Approximation

    Ideal School and College

  5. Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework

    May 29, 2026Zaiwei Chen, Siva Theja MaguluriLyapunov StabilityStochastic Approximation

    Purdue IE · Georgia Tech IE

  6. Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning

    May 29, 2026Vagul Mahadevan, Claire Chen, Shuze Daniel Liu +1Stochastic ApproximationTemporal-Difference Learning

    Metron, Reston, VA, USA · Division Office Physics, Math and Astronomy, California Institute of Technology, Pasadena, CA, USA · Data Science Lab, MIT, Cambridge, MA, USA +2

  7. Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training

    May 25, 2026Maxim Bolshim, Alexander KugaevskikhNeural Network MemorizationStochastic Approximation

    ITMO University, Saint Petersburg, Russia · ITMO University, St. Petersburg, Russia

  8. Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise

    May 20, 2026Shubhada Agrawal, Siva Theja Maguluri, Martin ZubeldiaHeavy-Tailed NoiseStochastic Approximation

    Indian Institute of Science · Georgia Institute of Technology · University of Minnesota

  9. Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation

    May 19, 2026Ilya Levin, Maksim Shuklin, Eric Moulines +2Stochastic ApproximationFederated Learning

    1HSE University, Russia · 2MBZUAI · 3CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France

  10. On Gaussian approximation for entropy-regularized Q-learning with function approximation

    May 17, 2026Artemy Rubtsov, Rahul Singh, Eric Moulines +2Reinforcement LearningStochastic Approximation

    HSE University, Moscow, Russia · Mohamed Bin Zayed University of AI, UAE · EPITA, France +1

  11. Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation

    May 9, 2026ZhiMing Li, Yue SongLatent Variable ModelsStochastic Approximation

    School of Computer Science and Technology TianJin University Tianjin 300350, China · College of AI Tsinghua University Beijing 100084, China

  12. Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL

    May 8, 2026Shengbo Wang, Zexi ZhangStochastic ApproximationDistributionally Robust RL

    Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California

  13. Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift

    May 8, 2026Xinyu Liu, Zixuan Xie, Shangtong ZhangReinforcement LearningStochastic Approximation

    University of Virginia

  14. A Sharp Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning

    May 7, 2026Ege C. Kaya, Abolfazl HashemiDistributional RLReinforcement Learning

    Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906

  15. Bridging the Gap Between Average and Discounted TD Learning

    May 3, 2026Haoxing Tian, Zaiwei Chen, Ioannis Ch. Paschalidis +1Reinforcement LearningAverage-Reward RL

    Department of Electrical and Computer Engineering Boston University · Edwardson School of Industrial Engineering Purdue University

  16. Inference of Online Newton Methods with Nesterov's Accelerated Sketching

    Apr 25, 2026Haoxuan Wang, Xinchen Du, Sen NaStochastic ApproximationSecond-Order Optimization

    School of Industrial and Systems Engineering, Georgia Institute of Technology.

  17. Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization

    Mar 13, 2026Noel Smith, Andrzej RuszczynskiStochastic OptimizationStochastic Approximation

    Department of Management Science and Information Systems, Rutgers University

  18. Constant-Stepsize Stochastic Approximation: Finite-Time Convergence, Gaussian Approximation, and Tail Bounds

    Feb 15, 2026Zedong Wang, Yuyang Wang, Ijay Narang +3Stochastic ApproximationStochastic Gradient Descent

    H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Mathematics, Brown University, Providence, RI 02912, USA · School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA +2

  19. Uniform-in-time convergence bounds for Persistent Contrastive Divergence algorithms

    Oct 2, 2025Paul Felix Valsecchi Oliva, O. Deniz Akyildiz, Andrew DuncanParameter EstimationStochastic Approximation

    Department of Mathematics, Imperial College London

  20. A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning

    Sep 14, 2025Jia-Qi Yang, Lei ShiStochastic ApproximationReproducing Kernel Hilbert Spaces

    School of Mathematical Sciences and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, China.

  21. Towards Weaker Variance Assumptions for Stochastic Optimization

    Apr 14, 2025Ahmet Alacaoglu, Yura Malitsky, Stephen J. WrightStochastic OptimizationLast-Iterate Convergence

    Department of Mathematics, University of British Columbia · Faculty of Mathematics, University of Vienna · Department of Computer Sciences, University of Wisconsin–Madison

  22. Nonasymptotic CLT and Error Bounds for Linear Two-Time-Scale Stochastic Approximation

    Feb 14, 2025Seo Taek Kong, Sihan Zeng, Thinh T. Doan +1Stochastic ApproximationTwo-Timescale Stochastic Approximation

    Coordinated Science Laboratory, Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801 USA · JP Morgan AI Research, Palo Alto, CA 94304 USA · Aerospace Engineering and Engineering Mechanics Department at University of Texas, Austin, Austin, TX 78712 USA

  23. Statistical Inference for Policy Evaluation with Temporal Difference Learning

    Oct 21, 2024Weichen Wu, Gen Li, Yuting Wei +1Parameter EstimationConfidence Region Estimation

    The Voleon Group, New York, NY, USA · Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR. · Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. +1