:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Bo, Wang, Wei, Ye, Peng
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.01291
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
by: Li, Bo, et al.
Published: (2025)

Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
by: Li, Bo, et al.
Published: (2024)

Sample-Near-Optimal Agnostic Boosting with Improved Running Time
by: da Cunha, Arthur, et al.
Published: (2026)

Near Optimal Non-asymptotic Sample Complexity of 1-Identification
by: Li, Zitian, et al.
Published: (2025)

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
by: Wei, Yukuan, et al.
Published: (2025)

Near-Optimal Sample Complexity for Online Constrained MDPs
by: Liu, Chang, et al.
Published: (2026)

Near-Optimal Private Tests for Simple and MLR Hypotheses
by: Chen, Yu-Wei, et al.
Published: (2026)

Nearly Optimal Sample Complexity for Learning with Label Proportions
by: Busa-Fekete, Robert, et al.
Published: (2025)

Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning
by: Li, Zhenghao, et al.
Published: (2025)

Nearly Optimal Differentially Private ReLU Regression
by: Ding, Meng, et al.
Published: (2025)

Sample-Optimal Agnostic Boosting with Unlabeled Data
by: Ghai, Udaya, et al.
Published: (2025)

Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning
by: Cipollone, Roberto, et al.
Published: (2025)

Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL
by: Zurek, Matthew, et al.
Published: (2025)

Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning
by: Kayal, Aya, et al.
Published: (2025)

Agnostic Active Learning of Single Index Models with Linear Sample Complexity
by: Gajjar, Aarshvi, et al.
Published: (2024)

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
by: Shi, Laixi, et al.
Published: (2022)

Model Agnostic Differentially Private Causal Inference
by: Lebeda, Christian Janos, et al.
Published: (2025)

Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity
by: Lau, Ivan, et al.
Published: (2025)

Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back
by: Cohen, Alon, et al.
Published: (2025)

Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
by: Deng, Zilong, et al.
Published: (2025)

Instance-Optimality for Private KL Distribution Estimation
by: Ye, Jiayuan, et al.
Published: (2025)

The Sample Complexity of Replicable Realizable PAC Learning
by: Larsen, Kasper Green, et al.
Published: (2026)

On the Sample Complexity of Differentially Private Policy Optimization
by: He, Yi, et al.
Published: (2025)

The Limits of Differential Privacy in Online Learning
by: Li, Bo, et al.
Published: (2024)

Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment
by: Hu, Bingshan, et al.
Published: (2021)

Exactly Minimax-Optimal Locally Differentially Private Sampling
by: Park, Hyun-Young, et al.
Published: (2024)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
by: Aliakbarpour, Maryam, et al.
Published: (2025)

Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
by: Sherman, Uri, et al.
Published: (2025)

Sample-Optimal Private Regression in Polynomial Time
by: Anderson, Prashanti, et al.
Published: (2025)

Near-Optimal Real-Time Personalization with Simple Transformers
by: An, Lin, et al.
Published: (2025)

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
by: Chen, Jiale, et al.
Published: (2025)

Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits
by: Ye, Zichun, et al.
Published: (2025)

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
by: Lowy, Andrew, et al.
Published: (2025)

Nearly Optimal Subdata Selection
by: Yang, Min, et al.
Published: (2026)

Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
by: Kornowski, Guy, et al.
Published: (2024)

The Optimal Sample Complexity of Multiclass and List Learning
by: Pabbaraju, Chirag
Published: (2026)

Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
by: Li, Long-Fei, et al.
Published: (2024)

Optimal Differentially Private Sampling of Unbounded Gaussians
by: Iverson, Valentio, et al.
Published: (2025)

Sample-Efficient Agnostic Boosting
by: Ghai, Udaya, et al.
Published: (2024)

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
by: Hanneke, Steve, et al.
Published: (2026)