Poster "sample complexity analysis" Papers
21 papers found
Conference
Finite-Time Bounds for Average-Reward Fitted Q-Iteration
Jongmin Lee, Ernest Ryu
FraPPE: Fast and Efficient Preference-Based Pure Exploration
Udvas Das, Apurv Shukla, Debabrota Basu
Learning Orthogonal Multi-Index Models: A Fine-Grained Information Exponent Analysis
Yunwei Ren, Jason Lee
Linear Mixture Distributionally Robust Markov Decision Processes
Zhishuai Liu, Pan Xu
Model-Free Offline Reinforcement Learning with Enhanced Robustness
Chi Zhang, Zain Ulabedeen Farhat, George Atia et al.
Offline Actor-Critic for Average Reward MDPs
William Powell, Jeongyeol Kwon, Qiaomin Xie et al.
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
Fan Chen, Zeyu Jia, Alexander Rakhlin et al.
Preference Elicitation for Offline Reinforcement Learning
Alizée Pace, Bernhard Schölkopf, Gunnar Ratsch et al.
Probably Approximately Precision and Recall Learning
Lee Cohen, Yishay Mansour, Shay Moran et al.
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF
Heyang Zhao, Chenlu Ye, Quanquan Gu et al.
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
Xuran Meng, Difan Zou, Yuan Cao
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
Jiin Woo, Laixi Shi, Gauri Joshi et al.
Federated Representation Learning in the Under-Parameterized Regime
Renpu Liu, Cong Shen, Jing Yang
Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm
Fuzhong Zhou, Chenyu Zhang, Xu Chen et al.
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds
Yuyang Zhang, Shahriar Talebi, Na Li
On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta et al.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, XiangCheng Zhang, Siwei Wang et al.
Risk-Sensitive Reward-Free Reinforcement Learning with CVaR
Xinyi Ni, Guanlin Liu, Lifeng Lai
Single-Trajectory Distributionally Robust Reinforcement Learning
Zhipeng Liang, Xiaoteng Ma, Jose Blanchet et al.