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Home/Authors/Yishay Mansour

Yishay Mansour

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 15, 2026, 5:45 AM AMS

28

papers

311

total citations

papers (28)

Prediction with Corrupted Expert Advice

NEURIPS 2020arXiv

Differentially Private Multi-Armed Bandits in the Shuffle Model

NEURIPS 2021arXiv

Minimax Regret for Stochastic Shortest Path

NEURIPS 2021arXiv

Sample Complexity of Uniform Convergence for Multicalibration

NEURIPS 2020arXiv

Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback

NEURIPS 2022arXiv

Benign Underfitting of Stochastic Gradient Descent

NEURIPS 2022arXiv

Principal-Agent Reward Shaping in MDPs

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity

NEURIPS 2020arXiv

Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure

NEURIPS 2021arXiv

Optimal Rates for Random Order Online Optimization

NEURIPS 2021arXiv

Rate-Optimal Policy Optimization for Linear Markov Decision Processes

Reinforcement Learning with Feedback Graphs

NEURIPS 2020arXiv

Eluder-based Regret for Stochastic Contextual MDPs

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

NEURIPS 2023arXiv

Fair Wrapping for Black-box Predictions

NEURIPS 2022arXiv

ROI Maximization in Stochastic Online Decision-Making

NEURIPS 2021arXiv

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback

NEURIPS 2023arXiv

Delay as Payoff in MAB

Probably Approximately Precision and Recall Learning

NEURIPS 2025arXiv

Batch Ensemble for Variance Dependent Regret in Stochastic Bandits

Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback

NEURIPS 2025arXiv

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation

Dueling Bandits with Team Comparisons

NEURIPS 2021arXiv

Adversarially Robust Streaming Algorithms via Differential Privacy

Black-Box Differential Privacy for Interactive ML

Finding Safe Zones of Markov Decision Processes Policies

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations