9 citations · 28 across the 11 of their papers we have counts for
13 papers
On Incentivized Exploration beyond Bayesianism and Full-Information
Dimitar Chakarov, Lee Cohen, Nathan Srebro
We extend Incentive Compatible Exploration beyond the Bayesian full-information setting of Kremer et al. [2014]. We consider agents that may possess external information unknown to…
Online Set Learning from Precision and Recall Feedback
Lee Cohen, Yishay Mansour, Shay Moran +1
We consider the problem of learning an unknown subset of a domain in an online setting. In each round , the learner predicts a set of items and receive…
Probably Approximately Precision and Recall Learning
Lee Cohen, Yishay Mansour, Shay Moran +1
Precision and Recall are fundamental metrics in machine learning tasks where both accurate predictions and comprehensive coverage are essential, such as in multi-label learning, la…
Learnability Gaps of Strategic Classification
Lee Cohen, Yishay Mansour, Shay Moran +1
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For i…
Incentivized Collaboration in Active Learning
Lee Cohen, Han Shao
In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for incentivized collaboration. Here, rat…
Sequential Strategic Screening
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
We initiate the study of strategic behavior in screening processes with multiple classifiers. We focus on two contrasting settings: a conjunctive setting in which an individual mus…
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao, Lee Cohen, Avrim Blum +3
In classic reinforcement learning (RL) and decision making problems, policies are evaluated with respect to a scalar reward function, and all optimal policies are the same with reg…
Modeling Attrition in Recommender Systems with Departing Bandits
Omer Ben-Porat, Lee Cohen, Liu Leqi +2
Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interactio…
Finding Safe Zones of policies Markov Decision Processes
Lee Cohen, Yishay Mansour, Michal Moshkovitz
Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a S…
Dueling Bandits with Team Comparisons
Lee Cohen, Ulrike Schmidt-Kraepelin, Yishay Mansour
We introduce the dueling teams problem, a new online-learning setting in which the learner observes noisy comparisons of disjoint pairs of -sized teams from a universe of pl…
Sample Complexity of Uniform Convergence for Multicalibration
Eliran Shabat, Lee Cohen, Yishay Mansour
There is a growing interest in societal concerns in machine learning systems, especially in fairness. Multicalibration gives a comprehensive methodology to address group fairness.…
Efficient candidate screening under multiple tests and implications for fairness
Lee Cohen, Zachary C. Lipton, Yishay Mansour
When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy sign…
Optimal Algorithm for Bayesian Incentive-Compatible Exploration
Lee Cohen, Yishay Mansour
We consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only in…