ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Frameworks and Results in Distribu- tionally Robust Optimization.Open Journal of Mathematical Optimization, 3: 4
6 Pith papers cite this work. Polarity classification is still indexing.
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Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
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.
EDRBO uses ensemble surrogates and Wasserstein ambiguity sets to robustify BO acquisition functions against context distribution mismatch, with sublinear regret O(γ_T √T) and SOTA empirical results on continuous contexts.
Unified framework for complex zero-sum games with chance constraints that converts probabilistic constraints into convex second-order cone programs under various distribution assumptions.
Robust optimization framework for green ammonia that ensures feasible capacity plans under renewable uncertainty where constraint aggregation fails, using scenario reduction and adaptive policies.
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