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Found 9 papers, 0 papers with code
Learnability Gaps of Strategic Classification
We essentially show that any learnable class is also strategically learnable: we first consider a fully informative setting, where the manipulation structure (which is modeled by a manipulation graph $G^\star$) is known and during training time the learner has access to both the pre-manipulation data and post-manipulation data.
Bayesian Strategic Classification
We initiate the study of partial information release by the learner in strategic classification.
Incentivized Collaboration in Active Learning
In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for incentivized collaboration.
Sequential Strategic Screening
We initiate the study of strategic behavior in screening processes with multiple classifiers.
Modeling Attrition in Recommender Systems with Departing Bandits
We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal.
Finding Safe Zones of policies Markov Decision Processes
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.
Dueling Bandits with Team Comparisons
We introduce the dueling teams problem, a new online-learning setting in which the learner observes noisy comparisons of disjoint pairs of $k$-sized teams from a universe of $n$ players.
Sample Complexity of Uniform Convergence for Multicalibration
There is a growing interest in societal concerns in machine learning systems, especially in fairness.
Efficient candidate screening under multiple tests and implications for fairness
We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests.
