Awesome Statistics Theory

Key papers

• Learning In Restless Bandits Under Exogenous Global Markov Process (2021)
  Tomer Gafni, Michal Yemini, Kobi Cohen
  6.34
• Anytime-valid Off-policy Inference For Contextual Bandits (2022)
  Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, et al.
  2.26
• Dynamic Decision-making Under Model Misspecification: A Stochastic Stability Approach (2026)
  Xinyu Dai, Daniel Chen, Yian Qian
  2.00
• Sparsity Is Necessary: Polynomial-time Stability For Agentic Llms In Large Action Spaces (2026)
  Angshul Majumdar
  2.00
• Notes On The Reward Representation Of Posterior Updates (2026)
  Pedro A. Ortega
  2.00
• Autocorrelation Effects In A Stochastic-process Model For Decision Making Via Time Series (2026)
  Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana, et al.
  2.00
• Asymptotically Optimal Regret In Communicating Markov Decision Processes (2025)
  Victor Boone
  1.33
• Scalable Policy Maximization Under Network Interference (2025)
  Aidan Gleich, Eric Laber, Alexander Volfovsky
  1.33
• Stability And Generalization For Bellman Residuals (2025)
  Enoch H. Kang, Kyoungseok Jang
  1.33
• Reinforcement Learning With Continuous Actions Under Unmeasured Confounding (2025)
  Yuhan Li, Eugene Han, Yifan Hu, et al.
  1.33
• LITE: Efficiently Estimating Gaussian Probability Of Maximality (2025)
  Nicolas Menet, Jonas Hübotter, Parnian Kassraie, et al.
  1.33
• Bayesian Decision Making Around Experts (2025)
  Daniel Jarne Ornia, Joel Dyer, Nicholas Bishop, et al.
  1.33
• Gap-dependent Bounds For Federated \(q\)-learning (2025)
  Haochen Zhang, Zhong Zheng, Lingzhou Xue
  1.33
• Sampling Complexity Of TD And PPO In RKHS (2025)
  Lu Zou, Wendi Ren, Weizhong Zhang, et al.
  1.33
• Batched Nonparametric Bandits via k-Nearest Neighbor UCB (2025)
  Sakshi Arya
  1.28
• Markov Decision Processes With Noisy State Observation (2023)
  Amirhossein Afsharrad, Sanjay Lall
  0.00
• Mitigating Partial Observability In Sequential Decision Processes Via The Lambda Discrepancy (2024)
  Cameron Allen, Aaron Kirtland, Ruo Yu Tao, et al.
  0.00
• Proximal Reinforcement Learning: Efficient Off-policy Evaluation In Partially Observed Markov Decision Processes (2021)
  Andrew Bennett, Nathan Kallus
  0.00
• Contextual Bandits And Optimistically Universal Learning (2022)
  Moise Blanchard, Steve Hanneke, Patrick Jaillet
  0.00
• Mechanisms For A No-regret Agent: Beyond The Common Prior (2020)
  Modibo Camara, Jason Hartline, Aleck Johnsen
  0.00
• Optimal Cooperative Multiplayer Learning Bandits With Noisy Rewards And No Communication (2023)
  William Chang, Yuanhao Lu
  0.00
• Debiasing Samples From Online Learning Using Bootstrap (2021)
  Ningyuan Chen, Xuefeng Gao, Yi Xiong
  0.00
• Optimal Policies For Observing Time Series And Related Restless Bandit Problems (2017)
  Christopher R. Dance, Tomi Silander
  0.00
• Solving Non-rectangular Reward-robust Mdps Via Frequency Regularization (2023)
  Uri Gadot, Esther Derman, Navdeep Kumar, et al.
  0.00
• A Relaxed Technical Assumption For Posterior Sampling-based Reinforcement Learning For Control Of Unknown Linear Systems (2021)
  Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan, et al.
  0.00
• Restless Bandit Problem With Rewards Generated By A Linear Gaussian Dynamical System (2024)
  Jonathan Gornet, Bruno Sinopoli
  0.00
• Learning For Bandits Under Action Erasures (2024)
  Osama Hanna, Merve Karakas, Lin F. Yang, et al.
  0.00
• Computing The Performance Of A New Adaptive Sampling Algorithm Based On The Gittins Index In Experiments With Exponential Rewards (2023)
  James K. He, Sofía S. Villar, Lida Mavrogonatou
  0.00
• On The Convergence Rate Of Off-policy Policy Optimization Methods With Density-ratio Correction (2021)
  Jiawei Huang, Nan Jiang
  0.00
• Optimal Convergence Rate For Exact Policy Mirror Descent In Discounted Markov Decision Processes (2023)
  Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
  0.00
• Convergence Of Finite Memory Q-learning For Pomdps And Near Optimality Of Learned Policies Under Filter Stability (2021)
  Ali Devran Kara, Serdar Yuksel
  0.00
• Delegative Reinforcement Learning: Learning To Avoid Traps With A Little Help (2019)
  Vanessa Kosoy
  0.00
• Bayesian Policy Optimization For Model Uncertainty (2018)
  Gilwoo Lee, Brian Hou, Aditya Mandalika, et al.
  0.00
• Improved Algorithm For Adversarial Linear Mixture Mdps With Bandit Feedback And Unknown Transition (2024)
  Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
  0.00
• Regret Minimization Experience Replay In Off-policy Reinforcement Learning (2021)
  Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang, et al.
  0.00
• Provable General Function Class Representation Learning In Multitask Bandits And Mdps (2022)
  Rui Lu, Andrew Zhao, Simon S. Du, et al.
  0.00
• Uncertainty Representations In State-space Layers For Deep Reinforcement Learning Under Partial Observability (2024)
  Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, et al.
  0.00
• Online Learning In Mdps With Linear Function Approximation And Bandit Feedback (2020)
  Gergely Neu, Julia Olkhovskaya
  0.00
• On State Variables, Bandit Problems And Pomdps (2020)
  Warren B Powell
  0.00
• Logarithmic Smoothing For Pessimistic Off-policy Evaluation, Selection And Learning (2024)
  Otmane Sakhi, Imad Aouali, Pierre Alquier, et al.
  0.00
• Rate-optimal Policy Optimization For Linear Markov Decision Processes (2023)
  Uri Sherman, Alon Cohen, Tomer Koren, et al.
  0.00
• Quantifying The Sensitivity Of Inverse Reinforcement Learning To Misspecification (2024)
  Joar Skalse, Alessandro Abate
  0.00
• Provably Efficient Imitation Learning From Observation Alone (2019)
  Wen Sun, Anirudh Vemula, Byron Boots, et al.
  0.00
• You May Not Need Ratio Clipping In PPO (2022)
  Mingfei Sun, Vitaly Kurin, Guoqing Liu, et al.
  0.00
• Policy Optimization Through Approximate Importance Sampling (2019)
  Marcin B. Tomczak, Dongho Kim, Peter Vrancx, et al.
  0.00
• Off-policy Evaluation And Learning From Logged Bandit Feedback: Error Reduction Via Surrogate Policy (2018)
  Yuan Xie, Boyi Liu, Qiang Liu, et al.
  0.00
• Deceptive Kernel Function On Observations Of Discrete POMDP (2020)
  Zhili Zhang, Quanyan Zhu
  0.00
• Learning Adversarial Low-rank Markov Decision Processes With Unknown Transition And Full-information Feedback (2023)
  Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, et al.
  0.00
• Optimistic Policy Optimization Is Provably Efficient In Non-stationary Mdps (2021)
  Han Zhong, Zhongren Chen, Zhuoran Yang, et al.
  0.00
• GEC: A Unified Framework For Interactive Decision Making In MDP, POMDP, And Beyond (2022)
  Han Zhong, Wei Xiong, Sirui Zheng, et al.
  0.00
• A Theoretical Analysis Of Optimistic Proximal Policy Optimization In Linear Markov Decision Processes (2023)
  Han Zhong, Tong Zhang
  0.00
• Finite-sample Analysis For SARSA With Linear Function Approximation (2019)
  Shaofeng Zou, Tengyu Xu, Yingbin Liang
  0.00
• Static Pricing: Universal Guarantees for Reusable Resources (2019)
  Omar Besbes et al.
  —
• How to Hire Secretaries with Stochastic Departures (2019)
  Thomas Kesselheim et al.
  —
• On Thompson Sampling for Smoother-than-Lipschitz Bandits (2020)
  James A. Grant and David S. Leslie
  —
• Adaptive Estimator Selection for Off-Policy Evaluation (2020)
  Yi Su et al.
  —
• Probability Learning based Tabu Search for the Budgeted Maximum Coverage Problem (2020)
  Liwen Li et al.
  —
• Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation (2020)
  Georgios Kotsalis et al.
  —
• A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (2020)
  Haoran Zhu et al.
  —
• Online Stochastic Optimization with Wasserstein Based Non-stationarity (2020)
  Jiashuo Jiang et al.
  —