Yishay Mansour

research

∙ 08/28/2023

Rate-Optimal Policy Optimization for Linear Markov Decision Processes

We study regret minimization in online episodic linear Markov Decision P...

0 Uri Sherman, et al. ∙

research

∙ 07/02/2023

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

We study a generalization of boosting to the multiclass setting. We intr...

0 Nataly Brukhim, et al. ∙

research

∙ 03/12/2023

The tree reconstruction game: phylogenetic reconstruction using reinforcement learning

We propose a reinforcement-learning algorithm to tackle the challenge of...

0 Dana Azouri, et al. ∙

research

∙ 03/02/2023

Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation

We present the OMG-CMDP! algorithm for regret minimization in adversaria...

0 Orin Levy, et al. ∙

research

∙ 02/27/2023

On Differentially Private Online Predictions

In this work we introduce an interactive variant of joint differential p...

0 Haim Kaplan, et al. ∙

research

∙ 02/03/2023

Pseudonorm Approachability and Applications to Regret Minimization

Blackwell's celebrated approachability theory provides a general framewo...

0 Christoph Dann, et al. ∙

research

∙ 02/01/2023

Uniswap Liquidity Provision: An Online Learning Approach

Decentralized Exchanges (DEXs) are new types of marketplaces leveraging ...

0 Yogev Bar-On, et al. ∙

research

∙ 01/30/2023

Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation

We study reinforcement learning with linear function approximation and a...

0 Uri Sherman, et al. ∙

research

∙ 01/29/2023

Concurrent Shuffle Differential Privacy Under Continual Observation

We introduce the concurrent shuffle model of differential privacy. In th...

0 Jay Tenenbaum, et al. ∙

research

∙ 11/27/2022

Counterfactual Optimism: Rate Optimal Regret for Stochastic Contextual MDPs

We present the UC^3RL algorithm for regret minimization in Stochastic Co...

0 Orin Levy, et al. ∙

research

∙ 07/28/2022

Regret Minimization and Convergence to Equilibria in General-sum Markov Games

An abundance of recent impossibility results establish that regret minim...

0 Liad Erez, et al. ∙

research

∙ 07/22/2022

Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP

We present regret minimization algorithms for stochastic contextual MDPs...

0 Orin Levy, et al. ∙

research

∙ 06/19/2022

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...

0 Christoph Dann, et al. ∙

research

∙ 06/09/2022

There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes

Interpretability is an essential building block for trustworthiness in r...

16 Yishay Mansour, et al. ∙

research

∙ 05/19/2022

What killed the Convex Booster ?

A landmark negative result of Long and Servedio established a worst-case...

0 Yishay Mansour, et al. ∙

research

∙ 05/17/2022

Strategizing against Learners in Bayesian Games

We study repeated two-player games where one of the players, the learner...

6 Yishay Mansour, et al. ∙

research

∙ 03/02/2022

Learning Efficiently Function Approximation for Contextual MDP

We study learning contextual MDPs using a function approximation for bot...

0 Orin Levy, et al. ∙

research

∙ 02/27/2022

Benign Underfitting of Stochastic Gradient Descent

We study to what extent may stochastic gradient descent (SGD) be underst...

0 Tomer Koren, et al. ∙

research

∙ 02/12/2022

Stochastic Strategic Patient Buyers: Revenue maximization using posted prices

We consider a seller faced with buyers which have the ability to delay t...

0 Eitan-Hai Mashiah, et al. ∙

research

∙ 02/11/2022

A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability

We study the problem of semi-supervised learning of an adversarially-rob...

0 Idan Attias, et al. ∙

research

∙ 02/10/2022

Monotone Learning

The amount of training-data is one of the key factors which determines t...

0 Olivier Bousquet, et al. ∙

research

∙ 01/31/2022

Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback

The standard assumption in reinforcement learning (RL) is that agents ob...

0 Tiancheng Jin, et al. ∙

research

∙ 01/31/2022

Cooperative Online Learning in Stochastic and Adversarial MDPs

We study cooperative online learning in stochastic and adversarial Marko...

0 Tal Lancewicki, et al. ∙

research

∙ 01/31/2022

Fair Wrapping for Black-box Predictions

We introduce a new family of techniques to post-process ("wrap") a black...

2 Alexander Soen, et al. ∙

research

∙ 12/29/2021

Differentially-Private Clustering of Easy Instances

Clustering is a fundamental problem in data analysis. In differentially ...

7 Edith Cohen, et al. ∙

research

∙ 12/06/2021

Nonstochastic Bandits with Composite Anonymous Feedback

We investigate a nonstochastic bandit setting in which the loss of an ac...

4 Nicolò Cesa-Bianchi, et al. ∙

research

∙ 11/07/2021

Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds

A dynamic algorithm against an adaptive adversary is required to be corr...

0 Amos Beimel, et al. ∙

research

∙ 10/19/2021

FriendlyCore: Practical Differentially Private Aggregation

Differentially private algorithms for common metric aggregation tasks, s...

10 Eliad Tsfadia, et al. ∙

research

∙ 06/29/2021

Optimal Rates for Random Order Online Optimization

We study online convex optimization in the random order model, recently ...

0 Uri Sherman, et al. ∙

research

∙ 06/22/2021

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations

There have been many recent advances on provably efficient Reinforcement...

10 Christoph Dann, et al. ∙

research

∙ 06/05/2021

Differentially Private Multi-Armed Bandits in the Shuffle Model

We give an (ε,δ)-differentially private algorithm for the multi-armed ba...

0 Jay Tenenbaum, et al. ∙

research

∙ 06/04/2021

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions

We study the stochastic Multi-Armed Bandit (MAB) problem with random del...

0 Tal Lancewicki, et al. ∙

research

∙ 03/24/2021

Minimax Regret for Stochastic Shortest Path

We study the Stochastic Shortest Path (SSP) problem in which an agent ha...

15 Alon Cohen, et al. ∙

research

∙ 03/15/2021

Competitive Equilibria with Unequal Budgets: Supporting Arbitrary Pareto Optimal Allocations

We consider a market setting of agents with additive valuations over het...

0 Nir Andelman, et al. ∙

research

∙ 01/31/2021

Online Markov Decision Processes with Aggregate Bandit Feedback

We study a novel variant of online finite-horizon Markov Decision Proces...

10 Alon Cohen, et al. ∙

research

∙ 01/26/2021

Separating Adaptive Streaming from Oblivious Streaming

We present a streaming problem for which every adversarially-robust stre...

0 Haim Kaplan, et al. ∙

research

∙ 12/29/2020

Learning Adversarial Markov Decision Processes with Delayed Feedback

Reinforcement learning typically assumes that the agent observes feedbac...

0 Tal Lancewicki, et al. ∙

research

∙ 10/27/2020

Adversarial Dueling Bandits

We introduce the problem of regret minimization in Adversarial Dueling B...

0 Aadirupa Saha, et al. ∙

research

∙ 10/04/2020

Kidney exchange and endless paths: On the optimal use of an altruistic donor

We consider a well-studied online random graph model for kidney exchange...

0 Avrim Blum, et al. ∙

research

∙ 10/02/2020

The Sparse Vector Technique, Revisited

We revisit one of the most basic and widely applicable techniques in the...

0 Haim Kaplan, et al. ∙

research

∙ 09/13/2020

Oracle-Efficient Reinforcement Learning in Factored MDPs with Unknown Structure

We consider provably-efficient reinforcement learning (RL) in non-episod...

0 Aviv Rosenberg, et al. ∙

research

∙ 08/21/2020

Beyond Individual and Group Fairness

We present a new data-driven model of fairness that, unlike existing sta...

8 Pranjal Awasthi, et al. ∙

research

∙ 07/24/2020

Detecting malicious PDF using CNN

Malicious PDF files represent one of the biggest threats to computer sec...

0 Raphael Fettaya, et al. ∙

research

∙ 07/19/2020

A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

We study multiple-source domain adaptation, when the learner has access ...

0 Yishay Mansour, et al. ∙

research

∙ 06/20/2020

Adversarial Stochastic Shortest Path

Stochastic shortest path (SSP) is a well-known problem in planning and c...

0 Aviv Rosenberg, et al. ∙

research

∙ 05/07/2020

Reinforcement Learning with Feedback Graphs

We study episodic reinforcement learning in Markov decision processes wh...

28 Christoph Dann, et al. ∙

research

∙ 04/16/2020

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

We present a differentially private learner for halfspaces over a finite...

0 Haim Kaplan, et al. ∙

research

∙ 04/13/2020

Adversarially Robust Streaming Algorithms via Differential Privacy

A streaming algorithm is said to be adversarially robust if its accuracy...

0 Avinatan Hassidim, et al. ∙

research

∙ 02/25/2020

Three Approaches for Personalization with Applications to Federated Learning

The standard objective in machine learning is to train a single model fo...

32 Yishay Mansour, et al. ∙

research

∙ 02/24/2020

Prediction with Corrupted Expert Advice

We revisit the fundamental problem of prediction with expert advice, in ...

0 Idan Amir, et al. ∙

Yishay Mansour

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