"sample complexity" Papers
29 papers found
Deployment Efficient Reward-Free Exploration with Linear Function Approximation
Zihan Zhang, Yuxin Chen, Jason Lee et al.
Formal Models of Active Learning from Contrastive Examples
Farnam Mansouri, Hans Simon, Adish Singla et al.
Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
Maryam Aliakbarpour, Zhan Shi, Ria Stevens et al.
On the Convergence of Single-Timescale Actor-Critic
Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi et al.
Streaming Federated Learning with Markovian Data
Khiem HUYNH, Malcolm Egan, Giovanni Neglia et al.
Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces
Ziyi Chen, Heng Huang
An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks
Zhifa Ke, Zaiwen Wen, Junyu Zhang
An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
Emre Sahinoglu, Shahin Shahrampour
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
Kihyuk Hong, Ambuj Tewari
A Theory of Fault-Tolerant Learning
Changlong Wu, Yifan Wang, Ananth Grama
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Zhan, Yixuan Wang et al.
Eliciting Kemeny Rankings
Anne-Marie George, Christos Dimitrakakis
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Jiabin Lin, Shana Moothedath, Namrata Vaswani
Faster Adaptive Decentralized Learning Algorithms
Feihu Huang, jianyu zhao
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Tianchen Zhou, Hairi, Haibo Yang et al.
From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers
Muhammed Emrullah Ildiz, Yixiao HUANG, Yingcong Li et al.
Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity
Marta Catalano, Hugo Lavenant
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson et al.
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games
Songtao Feng, Ming Yin, Yu-Xiang Wang et al.
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Lei Zhao, Mengdi Wang, Yu Bai
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
Multi-group Learning for Hierarchical Groups
Samuel Deng, Daniel Hsu
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
Gavin Brown, Krishnamurthy Dvijotham, Georgina Evans et al.
Replicable Learning of Large-Margin Halfspaces
Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen et al.
Reward-Free Kernel-Based Reinforcement Learning
Sattar Vakili, Farhang Nabiei, Da-shan Shiu et al.
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh
Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks
Lorenzo Bardone, Sebastian Goldt
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu et al.
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu et al.