Image de couverture de livre

Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Wibisono, Andre
Format:	Preprint
Publié:	2025
Sujets:	Information Theory Machine Learning Optimization and Control Statistics Theory
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Fast Convergence of $Φ$-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler
par: Mitra, Siddharth, et autres
Publié: (2024)

Ensemble-Conditional Gaussian Processes (Ens-CGP): Representation, Geometry, and Inference
par: Ravela, Sai, et autres
Publié: (2026)

Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of $Φ$-Mutual Information
par: Liang, Jiaming, et autres
Publié: (2024)

Long-time dynamics and universality of nonconvex gradient descent
par: Han, Qiyang
Publié: (2025)

Optimal transport natural gradient for statistical manifolds with continuous sample space
par: Chen, Yifan, et autres
Publié: (2018)

High-probability sample complexities for policy evaluation with linear function approximation
par: Li, Gen, et autres
Publié: (2023)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
par: Li, Gen, et autres
Publié: (2021)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
par: Li, Gen, et autres
Publié: (2020)

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
par: Bravo, Mario, et autres
Publié: (2025)

Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity
par: Stöger, Dominik, et autres
Publié: (2024)

Gradient descent inference in empirical risk minimization
par: Han, Qiyang, et autres
Publié: (2024)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
par: Roy, Somjit, et autres
Publié: (2026)

Quickest Change Detection with Confusing Change
par: Chen, Yu-Zhen Janice, et autres
Publié: (2024)

The Local Landscape of Phase Retrieval Under Limited Samples
par: Liu, Kaizhao, et autres
Publié: (2023)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation
par: Zarifis, Nikos, et autres
Publié: (2025)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
par: Zhu, Linglingzhi, et autres
Publié: (2025)

A Spectral Framework for Closed-Form Relative Density Estimation
par: Bach, Francis
Publié: (2026)

Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
par: Agterberg, Joshua, et autres
Publié: (2022)

Complexity of Non-Log-Concave Sampling in Fisher Information
par: Chewi, Sinho, et autres
Publié: (2026)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
par: Saldi, Naci, et autres
Publié: (2025)

Robust Assortment Optimization from Observational Data
par: Lu, Miao, et autres
Publié: (2026)

Learning an Optimal Assortment Policy under Observational Data
par: Han, Yuxuan, et autres
Publié: (2025)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
par: Bennouna, Amine, et autres
Publié: (2021)

Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
par: Chang, Serina, et autres
Publié: (2024)

Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL
par: Zurek, Matthew, et autres
Publié: (2025)

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
par: Lin, Meixia, et autres
Publié: (2025)

Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
par: Liao, Aaron R., et autres
Publié: (2026)

On Regularization via Early Stopping for Least Squares Regression
par: Sonthalia, Rishi, et autres
Publié: (2024)

Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent
par: Karnik, Santhosh, et autres
Publié: (2024)

Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
par: He, Yixuan, et autres
Publié: (2025)

Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences
par: Aolaritei, Liviu, et autres
Publié: (2025)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
par: Jiang, Yiheng, et autres
Publié: (2023)

Joint learning of a network of linear dynamical systems via total variation penalization
par: Donnat, Claire, et autres
Publié: (2025)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
par: Cheng, Xiuyuan, et autres
Publié: (2023)

Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
par: Maros, Marie, et autres
Publié: (2022)

Hyperparameter tuning via trajectory predictions: Stochastic prox-linear methods in matrix sensing
par: Lou, Mengqi, et autres
Publié: (2024)

Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
par: Paik, Seunghoon, et autres
Publié: (2025)

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
par: Li, Yingying, et autres
Publié: (2023)

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
par: Vukovic, Manojlo, et autres
Publié: (2026)

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
par: Yu, Qian, et autres
Publié: (2024)