Stochastic

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Period ending 2026-09-21

3 new papers

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Period ending 2026-09-14

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94 papers

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Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
May 14, 2026Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3Variance Reduction Stochastic Optimization
Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
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Scalable Solution of the Stochastic Multi-path Traveling Salesman Problem via Neural Networks
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The Great Pretender: A Stochasticity Problem in LLM Jailbreak
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Adaptive Multi-Round Allocation with Stochastic Arrivals
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Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
May 12, 2026Jingduo Pan, Taoran Wu, Yiling Xue +1Constrained Reinforcement Learning Bellman Equation
Deterministic Decomposition of Stochastic Generative Dynamics
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Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
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Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
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Model-Driven Policy Optimization in Differentiable Simulators via Stochastic Exploration
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Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
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Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
May 5, 2026Diego MarcondesPartial Differential Equation Benchmarks Logarithmic Sobolev Inequalities
Stochastic Sparse Attention for Memory-Bound Inference
May 3, 2026Kyle Lee, Corentin Delacour, Kevin Callahan-Coray +5Dynamic Sparse Attention LLM Inference Efficiency
Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
May 3, 2026Youngmin OhBandits Delay
Unlearning Offline Stochastic Multi-Armed Bandits
May 1, 2026Zichun Ye, Runqi Wang, Xuchuang Wang +3Unlearning Aims Multi-Armed Bandits
Generalization Bounds of Spiking Neural Networks via Rademacher Complexity
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Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss
Apr 25, 2026Mark Chua, Samuel AjilaSoftware Engineering Stochastic
A Green-Integral-Constrained Neural Solver with Stochastic Physics-Informed Regularization
Apr 23, 2026Mohammad Mahdi Abedi, David Pardo, Tariq AlkhalifahConventional Physics-Informed Neural Networks Neural Solvers
SGD at the Edge of Stability: The Stochastic Sharpness Gap
Apr 22, 2026Fangshuo Liao, Afroditi Kolomvaki, Anastasios KyrillidisStochastic Gradient Descent Sharpness-Aware Minimization
Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees
Apr 22, 2026Xueyan Li, Johannes Zenn, Ekaterina Fadeeva +3Efficient Inference Reasoning Traces
Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model
Apr 21, 2026Manuel Fernandez, Ludovic Stephan, Yizhe ZhuHypergraphs Image Clustering
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Apr 20, 2026Gonzalo Gonzalez-Pumariega, Saaket Agashe, Jiachen Yang +2Computer-Use Agents Unreliable Reasoning
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Apr 17, 2026Weijiang Xiong, Robert Fonod, Nikolas GeroliminisAccurate Traffic Forecasting Traffic
Stochasticity in Tokenisation Improves Robustness
Apr 17, 2026Sophie Steger, Rui Li, Sofiane Ennadir +4Tokenization Stochastic
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Apr 16, 2026Dingzhi Yu, Rui Pan, Yuxing Liu +1Stochastic Gradient Descent Optimizer Design
Best of both worlds: Stochastic & adversarial best-arm identification
Apr 16, 2026Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2Bandits Stochastic
Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
Mar 16, 2026Quoc Tran-Dinh, Nghia Nguyen-TrungVariance Reduction Stochastic
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
Mar 16, 2026Jeffrey D. VarnerSequence Modeling Feature Alignment
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Feb 3, 2026Hiroki Naganuma, Shagun Gupta, Youssef Briki +4Batch Size Stochastic
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Sep 24, 2025Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni +2Markov Decision Processes O(T^Β)$ Simultaneous Regret
Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models
Jun 25, 2025Xinghao Dong, Huchen Yang, Jin-Long WuMultiscale Dynamics Nonlinear Dynamics
The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
Jun 13, 2025Bernardo P. Schaeffer, Ricardo M. S. Rosa, Glauco ValleScore-Based Diffusion Model Stochastic Differential Equations
Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum
Jun 18, 2024Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. SayedStochastic Smooth Convex Optimization Convex Optimization
XConv: Low-memory stochastic backpropagation for convolutional layers
Jun 13, 2021Anirudh Thatipelli, Jeffrey Sam, Mathias Louboutin +3Convolutional Backpropagation

May 14, 2026 · cs.LGJ/K move · Enter open · S save

Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation

Zhankun Luo, Antesh Upadhyay, M. Berk Sahin, Sang Bin Moon+2

Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as momentum, SPIDER, STORM, and PAGE have been highly successful, their analyses are largely estimator-specific and expectation-based, obscuring the structural tradeoffs that determine reliability. In this paper, we develop a unified framework for stochastic variance-reduced estimation based on a recursion with three components: memory retention, reset probability, and a correction term for iterate movement. This framework recovers several classical estimators, motivates new second-order variants, and yields a bias-variance decomposition of estimation error. Our main result is a unified high-probability bound proved using a new dimension-free vector-valued Freedman inequality, valid for smooth normed spaces involving random sums of vector martingales. The result applies in both Euclidean and non-Euclidean settings, including the analysis of mirror-descent-based methods in Banach spaces. As applications, we obtain high-probability oracle complexities for unconstrained optimization with mirror descent, establishing the logarithmic dependence on the confidence level. We also derive the first

\tilde{\mathcal{O}}(\varepsilon^{-3})

oracle-complexity bounds for stochastic optimization with expectation constraints, improving upon the existing

\tilde{\mathcal{O}}(\varepsilon^{-4})

complexity by leveraging variance-reduced estimation for the first time in this setting.

Variance Reduction Stochastic Optimization Stochastic Conformal Test Martingales Inequalities Probability

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