Yishay Mansour

FriendlyCore: Practical Differentially Private Aggregation

2021 · arXiv (Cornell University)

Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large number of data points that is required for accurate results. …

Learning-Augmented Algorithms with Explicit Predictors

2024 · arXiv (Cornell University)

Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also …

Of Dice and Games: A Theory of Generalized Boosting

2024 · arXiv (Cornell University)

Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a false negative prediction can lead to worse consequences than a false …

Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning

2025 · arXiv (Cornell University)

We study reinforcement learning (RL) in the agnostic policy learning setting, where the goal is to find a policy whose performance is competitive with the best policy in a given class of interest $Π$ -- …

Three Approaches for Personalization with Applications to Federated Learning

2020 · arXiv (Cornell University)

The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model …