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Found 5 papers, 1 papers with code
Mistake, Manipulation and Margin Guarantees in Online Strategic Classification
We prove convergence, finite mistake and finite manipulation guarantees for a variety of agent cost structures.
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits
While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users.
Constrained Optimization of Rank-One Functions with Indicator Variables
We then demonstrate that extended representations of sets associated with epigraphs of rank-one convex functions over constraints modeling indicator relations naturally admit such a conic representation.
Risk Guarantees for End-to-End Prediction and Optimization Processes
While it is usually believed that good prediction performance in estimating the parameters will result in good subsequent optimization performance, formal theoretical guarantees on this are notably lacking.
Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
We study the problem of online learning (OL) from revealed preferences: a learner wishes to learn a non-strategic agent's private utility function through observing the agent's utility-maximizing actions in a changing environment.
