Statistical analysis of Inverse Entropy-regularized Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Belomestny, Denis, Naumov, Alexey, Samsonov, Sergey |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)
by: Puchkin, Nikita, et al.
Published: (2021)
Theoretical guarantees for neural control variates in MCMC
by: Belomestny, Denis, et al.
Published: (2023)
by: Belomestny, Denis, et al.
Published: (2023)
Tight Bounds for Schrödinger Potential Estimation in Unpaired Data Translation
by: Puchkin, Nikita, et al.
Published: (2025)
by: Puchkin, Nikita, et al.
Published: (2025)
Schrödinger bridge problem via empirical risk minimization
by: Belomestny, Denis, et al.
Published: (2026)
by: Belomestny, Denis, et al.
Published: (2026)
UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
by: Belomestny, Denis, et al.
Published: (2021)
by: Belomestny, Denis, et al.
Published: (2021)
Sample complexity of Schrödinger potential estimation
by: Puchkin, Nikita, et al.
Published: (2025)
by: Puchkin, Nikita, et al.
Published: (2025)
On the Upper Bounds for the Matrix Spectral Norm
by: Naumov, Alexey, et al.
Published: (2025)
by: Naumov, Alexey, et al.
Published: (2025)
High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation
by: Levin, Ilya, et al.
Published: (2025)
by: Levin, Ilya, et al.
Published: (2025)
Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)
by: Samsonov, Sergey, et al.
Published: (2025)
A note on concentration inequalities for the overlapped batch mean variance estimators for Markov chains
by: Moulines, Eric, et al.
Published: (2025)
by: Moulines, Eric, et al.
Published: (2025)
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
by: Sheshukova, Marina, et al.
Published: (2025)
by: Sheshukova, Marina, et al.
Published: (2025)
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)
by: Butyrin, Bogdan, et al.
Published: (2025)
Rosenthal-type inequalities for linear statistics of Markov chains
by: Durmus, Alain, et al.
Published: (2023)
by: Durmus, Alain, et al.
Published: (2023)
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
by: Samsonov, Sergey, et al.
Published: (2024)
by: Samsonov, Sergey, et al.
Published: (2024)
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
by: Sheshukova, Marina, et al.
Published: (2024)
by: Sheshukova, Marina, et al.
Published: (2024)
Statistical Inverse Problems in Hilbert Scales
by: Rastogi, Abhishake
Published: (2022)
by: Rastogi, Abhishake
Published: (2022)
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
by: van der Laan, Lars, et al.
Published: (2025)
by: van der Laan, Lars, et al.
Published: (2025)
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
by: Butyrin, Bogdan, et al.
Published: (2025)
by: Butyrin, Bogdan, et al.
Published: (2025)
On Gaussian approximation for entropy-regularized Q-learning with function approximation
by: Rubtsov, Artemy, et al.
Published: (2026)
by: Rubtsov, Artemy, et al.
Published: (2026)
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
by: Lin, Licong, et al.
Published: (2025)
by: Lin, Licong, et al.
Published: (2025)
Statistical Learning Theory for Distributional Classification
by: Fiedler, Christian
Published: (2026)
by: Fiedler, Christian
Published: (2026)
Statistical and Algorithmic Foundations of Reinforcement Learning
by: Chi, Yuejie, et al.
Published: (2025)
by: Chi, Yuejie, et al.
Published: (2025)
Multitask Learning and Bandits via Robust Statistics
by: Xu, Kan, et al.
Published: (2021)
by: Xu, Kan, et al.
Published: (2021)
Statistical Learning Guarantees for Group-Invariant Barron Functions
by: Yang, Yahong, et al.
Published: (2025)
by: Yang, Yahong, et al.
Published: (2025)
Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
by: Whitehouse, Justin, et al.
Published: (2026)
by: Whitehouse, Justin, et al.
Published: (2026)
Learning from Samples: Inverse Problems over measures via Sharpened Fenchel-Young Losses
by: Andrade, Francisco, et al.
Published: (2025)
by: Andrade, Francisco, et al.
Published: (2025)
Gaussian Approximation for Asynchronous Q-learning
by: Rubtsov, Artemy, et al.
Published: (2026)
by: Rubtsov, Artemy, et al.
Published: (2026)
Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory
by: Watanabe, Sumio
Published: (2024)
by: Watanabe, Sumio
Published: (2024)
The Central Role of the Loss Function in Reinforcement Learning
by: Wang, Kaiwen, et al.
Published: (2024)
by: Wang, Kaiwen, et al.
Published: (2024)
Extrapolation in Statistical Learning with Extreme Value Theory
by: Engelke, Sebastian, et al.
Published: (2026)
by: Engelke, Sebastian, et al.
Published: (2026)
Statistical optimal transport
by: Chewi, Sinho, et al.
Published: (2024)
by: Chewi, Sinho, et al.
Published: (2024)
Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps
by: Ma, Wanteng, et al.
Published: (2024)
by: Ma, Wanteng, et al.
Published: (2024)
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
by: Suh, Namjoon, et al.
Published: (2024)
by: Suh, Namjoon, et al.
Published: (2024)
Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
Complexity Dependent Error Rates for Physics-informed Statistical Learning via the Small-ball Method
by: Marcondes, Diego
Published: (2025)
by: Marcondes, Diego
Published: (2025)
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
by: Guo, Zijian, et al.
Published: (2025)
by: Guo, Zijian, et al.
Published: (2025)
Statistical Inference under Performativity
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Statistically guided deep learning
by: Kohler, Michael, et al.
Published: (2025)
by: Kohler, Michael, et al.
Published: (2025)
Statistical and computational challenges in ranking
by: Carpentier, Alexandra, et al.
Published: (2025)
by: Carpentier, Alexandra, et al.
Published: (2025)
Similar Items
-
Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021) -
Theoretical guarantees for neural control variates in MCMC
by: Belomestny, Denis, et al.
Published: (2023) -
Tight Bounds for Schrödinger Potential Estimation in Unpaired Data Translation
by: Puchkin, Nikita, et al.
Published: (2025) -
Schrödinger bridge problem via empirical risk minimization
by: Belomestny, Denis, et al.
Published: (2026) -
UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
by: Belomestny, Denis, et al.
Published: (2021)