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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.
Projection-Free Online Convex Optimization with Stochastic Constraints
This paper develops projection-free algorithms for online convex optimization with stochastic constraints.
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.
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity
Inspired by this observation, we show that, for a certain class of distributions, the only stationary point of the regularized ramp loss minimization problem is the global minimizer.
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