332 citations
- Tel Aviv UniversityIL25 papers
- Google (United States)US19 papers
- Technion – Israel Institute of TechnologyIL13 papers
- Hebrew University of JerusalemIL7 papers
- Bar-Ilan UniversityIL3 papers
- Weizmann Institute of ScienceIL3 papers
- Ben-Gurion University of the NegevIL2 papers
- Cornell UniversityUS2 papers
- Massachusetts Institute of TechnologyUS2 papers
- Microsoft (United States)US2 papers
- University of PennsylvaniaUS2 papers
- Aix-Marseille UniversitéFR1 paper
7 papers · 2 filters
Differentially-Private Clustering of Easy Instances
Edith Cohen, Haim Kaplan, Yishay Mansour +2
Clustering is a fundamental problem in data analysis. In differentially private clustering, the goal is to identify cluster centers without disclosing information on individual…
Nonstochastic Bandits with Composite Anonymous Feedback
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni +2
We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over the subsequent rounds in an adversaria…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
Tal Lancewicki, Shahar Segal, Tomer Koren +1
We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the reward-dependent delay setting,…
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson +4
Recent work has highlighted the role of initialization scale in determining the structure of the solutions that gradient methods converge to. In particular, it was shown that large…
Multiplicative Reweighting for Robust Neural Network Optimization
Noga Bar, Tomer Koren, Raja Giryes
Neural networks are widespread due to their powerful performance. Yet, they degrade in the presence of noisy labels at training time. Inspired by the setting of learning with exper…