Establishes O(d² δ^{-3} ε^{-3}) SZO complexity to reach (δ,ε)-Goldstein stationary points in non-smooth non-convex stochastic zeroth-order optimization with decision-dependent distributions, plus improved rates for smooth and Hessian-Lipschitz cases.
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Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
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Stochastic Non-Smooth Non-Convex Optimization with Decision-Dependent Distributions
Establishes O(d² δ^{-3} ε^{-3}) SZO complexity to reach (δ,ε)-Goldstein stationary points in non-smooth non-convex stochastic zeroth-order optimization with decision-dependent distributions, plus improved rates for smooth and Hessian-Lipschitz cases.
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Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.
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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.