← Authors

Yishay Mansour

115 papers · 2003–2025 · 9 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (10) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (30) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (22) 🐝 Cross-Pollinator (14) 🏠 Conference Loyalist (34) 🌟 Keyword Trendsetter Combo (3) πŸ”¬ Deep Specialist (11) 🀝 Dynamic Duo (21) 🌱 Topic Pioneer πŸ† Keyword Champion πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (110) πŸ’Ž Century Club (115) ⚑ Prolific Year (14) πŸ”₯ Unstoppable (14) πŸ“ˆ Trend Setter

Conferences

NIPS (34) ICML (27) COLT (21) ALT (10) AAAI (9) JMLR (7) AISTATS (3) IJCAI (3) UAI (1)

Top co-authors

Tomer Koren (21) Haim Kaplan (19) Alon Cohen (14) Shay Moran (12) Uri Stemmer (11) NicolΓ² Cesa-bianchi (10) Mehryar Mohri (10) Aviv Rosenberg (9) Roi Livni (8) Tal Lancewicki (7)

Research topics

Differential Privacy (4)

Keywords

regret bound (37) multi-armed bandit (18) online learning (18) sample complexity (15) markov decision process (11) reinforcement learning (10) differential privacy (10) online algorithm (9) regret minimization (9) adversarial learning (8) generalization bound (8) pac learning (8) stochastic optimization (7) bandit feedback (5) vc dimension (4) stochastic shortest path (4) multiclass classification (4) learning theory (4) agnostic learning (4) delayed feedback (4)

Papers

A Fine-grained Characterization of PAC Learnability COLT 2025
Delay as Payoff in MAB AAAI 2025
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits AAAI 2025
Rate-Preserving Reductions for Blackwell Approachability COLT 2025
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation ICML 2025
Non-stochastic Bandits With Evolving Observations ALT 2025
Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback ICML 2025
Dueling Convex Optimization with General Preferences ICML 2025
Of Dice and Games: A Theory of Generalized Boosting COLT 2025
Principal-Agent Reward Shaping in MDPs AAAI 2024
Fast Rates for Bandit PAC Multiclass Classification NIPS 2024
Learning-Augmented Algorithms with Explicit Predictors NIPS 2024
How to Boost Any Loss Function NIPS 2024
The Real Price of Bandit Information in Multiclass Classification COLT 2024
Learnability Gaps of Strategic Classification COLT 2024
Partially Interpretable Models with Guarantees on Coverage and Accuracy ALT 2024
Eluder-based Regret for Stochastic Contextual MDPs ICML 2024
Rate-Optimal Policy Optimization for Linear Markov Decision Processes ICML 2024
A Theory of Interpretable Approximations COLT 2024
Faster Convergence with MultiWay Preferences AISTATS 2024
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice ICML 2023
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation ICML 2023
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria NIPS 2023
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback NIPS 2023
Finding Safe Zones of Markov Decision Processes Policies NIPS 2023
Black-Box Differential Privacy for Interactive ML NIPS 2023
Optimism in Face of a Context:Regret Guarantees for Stochastic Contextual MDP AAAI 2023
Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers AAAI 2023
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation ICML 2023
Regret Minimization and Convergence to Equilibria in General-sum Markov Games ICML 2023
Reinforcement Learning Can Be More Efficient with Multiple Rewards ICML 2023
Concurrent Shuffle Differential Privacy Under Continual Observation ICML 2023
Pseudonorm Approachability and Applications to Regret Minimization ALT 2023
FriendlyCore: Practical Differentially Private Aggregation ICML 2022
Cooperative Online Learning in Stochastic and Adversarial MDPs ICML 2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation ICML 2022
Benign Underfitting of Stochastic Gradient Descent NIPS 2022
Fair Wrapping for Black-box Predictions NIPS 2022
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability NIPS 2022
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback NIPS 2022
Strategizing against Learners in Bayesian Games COLT 2022
Monotone Learning COLT 2022
Modeling Attrition in Recommender Systems with Departing Bandits AAAI 2022
Nonstochastic Bandits with Composite Anonymous Feedback JMLR 2022
Learning Adversarial Markov Decision Processes with Delayed Feedback AAAI 2022
Improved Generalization Bounds for Adversarially Robust Learning JMLR 2022
Differentially Private Multi-Armed Bandits in the Shuffle Model NIPS 2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations NIPS 2021
Dueling Bandits with Team Comparisons NIPS 2021
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure NIPS 2021
ROI Maximization in Stochastic Online Decision-Making NIPS 2021
The Sparse Vector Technique, Revisited COLT 2021
Online Markov Decision Processes with Aggregate Bandit Feedback COLT 2021
Optimal Rates for Random Order Online Optimization NIPS 2021
Stochastic Shortest Path with Adversarially Changing Costs IJCAI 2021
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data AISTATS 2021
Dueling Convex Optimization ICML 2021
Adversarial Dueling Bandits ICML 2021
Minimax Regret for Stochastic Shortest Path NIPS 2021
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions ICML 2021
Differentially-Private Clustering of Easy Instances ICML 2021
Prediction with Corrupted Expert Advice NIPS 2020
Adversarially Robust Streaming Algorithms via Differential Privacy NIPS 2020
Sample Complexity of Uniform Convergence for Multicalibration NIPS 2020
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NIPS 2020
Reinforcement Learning with Feedback Graphs NIPS 2020
Designing Committees for Mitigating Biases AAAI 2020
Apprenticeship Learning via Frank-Wolfe AAAI 2020
Thompson Sampling for Adversarial Bit Prediction ALT 2020
Top-$k$ Combinatorial Bandits with Full-Bandit Feedback ALT 2020
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies ALT 2020
Privately Learning Thresholds: Closing the Exponential Gap COLT 2020
Near-optimal Regret Bounds for Stochastic Shortest Path ICML 2020
Online Revenue Maximization for Server Pricing IJCAI 2020
Unknown mixing times in apprenticeship and reinforcement learning UAI 2020
Competitive ratio vs regret minimization: achieving the best of both worlds ALT 2019
Adversarial Online Learning with noise ICML 2019
Online Convex Optimization in Adversarial Markov Decision Processes ICML 2019
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits NIPS 2019
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function NIPS 2019
Graph-based Discriminators: Sample Complexity and Expressiveness NIPS 2019
Improved Generalization Bounds for Robust Learning ALT 2019
Delay and Cooperation in Nonstochastic Bandits JMLR 2019
Learning to Screen NIPS 2019
Learning Linear-Quadratic Regulators Efficiently with only $\sqrtT$ Regret ICML 2019
Differentially Private Learning of Geometric Concepts ICML 2019
Nonstochastic Bandits with Composite Anonymous Feedback COLT 2018
Discriminative Learning of Prediction Intervals AISTATS 2018
Planning and Learning with Stochastic Action Sets IJCAI 2018
Robust Inference for Multiclass Classification ALT 2018
Online Linear Quadratic Control ICML 2018
Learning Decision Trees with Stochastic Linear Classifiers ALT 2018
Efficient Co-Training of Linear Separators under Weak Dependence COLT 2017
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues NIPS 2017
Multi-Armed Bandits with Metric Movement Costs NIPS 2017
Bandits with Movement Costs and Adaptive Pricing COLT 2017
Efficient PAC Learning from the Crowd COLT 2017
Online Pricing with Strategic and Patient Buyers NIPS 2016
Delay and Cooperation in Nonstochastic Bandits COLT 2016
Online Learning with Low Rank Experts COLT 2016
Classification with Low Rank and Missing Data ICML 2015
Learning and inference in the presence of corrupted inputs COLT 2015
On the Complexity of Learning with Kernels COLT 2015
Thompson Sampling for Complex Online Problems ICML 2014
From Bandits to Experts: A Tale of Domination and Independence NIPS 2013
Exploiting Ontology Structures and Unlabeled Data for Learning ICML 2013
Regret Minimization for Branching Experts COLT 2013
Distributed Learning, Communication Complexity and Privacy COLT 2012
Learning Multiple Tasks using Shared Hypotheses NIPS 2012
Learning Bounds for Importance Weighting NIPS 2010
Domain Adaptation with Multiple Sources NIPS 2008
From External to Internal Regret JMLR 2007
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems JMLR 2006
Concentration Bounds for Unigram Language Models JMLR 2005
Learning Rates for Q-learning JMLR 2003