Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
Frameworks and results in distributionally robust optimization.Open Journal of Mathematical Optimization, 3:1–85, 2022
4 Pith papers cite this work. Polarity classification is still indexing.
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Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.
A tractable ensemble distributionally robust Bayesian optimization method achieves improved sublinear regret bounds under context uncertainty.
Unified framework for complex zero-sum games with chance constraints that converts probabilistic constraints into convex second-order cone programs under various distribution assumptions.
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Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
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