Introduces PZOS partial zeroth-order algorithm for MPECs that exploits leader's white-box cost information to achieve lower variance than full black-box zeroth-order methods, with convergence to partial Goldstein stationary points and empirical gains on routing and security games.
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback.Journal of Machine Learning Research, 18(52):1–11
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Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.
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Black-Box Followers, White-Box Leaders: Partial Zeroth-Order Methods for MPECs
Introduces PZOS partial zeroth-order algorithm for MPECs that exploits leader's white-box cost information to achieve lower variance than full black-box zeroth-order methods, with convergence to partial Goldstein stationary points and empirical gains on routing and security games.
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Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.