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Likelihood ratio confidence sets for sequential decision making
Certifiable, adaptive uncertainty estimates for unknown quantities are an essential
ingredient of sequential decision-making algorithms. Standard approaches rely on problem …
ingredient of sequential decision-making algorithms. Standard approaches rely on problem …
Improved regret bounds of (multinomial) logistic bandits via regret-to-confidence-set conversion
Logistic bandit is a ubiquitous framework of modeling users' choices, eg, click vs. no click for
advertisement recommender system. We observe that the prior works overlook or neglect …
advertisement recommender system. We observe that the prior works overlook or neglect …
Active exploration via experiment design in markov chains
A key challenge in science and engineering is to design experiments to learn about some
unknown quantity of interest. Classical experimental design optimally allocates the …
unknown quantity of interest. Classical experimental design optimally allocates the …
Diversified sampling for batched Bayesian optimization with determinantal point processes
Abstract In Bayesian Optimization (BO) we study black-box function optimization with noisy
point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by …
point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by …
A unified confidence sequence for generalized linear models, with applications to bandits
Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network
Car-hailing services play an important role in the modern transportation system, and the
utilities of the service providers highly depend on the efficiency of route planning algorithms …
utilities of the service providers highly depend on the efficiency of route planning algorithms …