3 accepted papers
Multi-objective optimization is a class of optimization problems with multiple conflicting objectives. We study offline optimization of multi-objective policies from data collected by a previously deployed policy. We propose a pessimistic estimator for policy values that can be easily plugged into e…
In this paper we provide simple, computationally efficient, active algorithms for completion of symmetric positive semidefinite matrices. Our proposed algorithms are based on adaptive Nyström sampling, and are allowed to actively query any element in the matrix, and obtain a possibly noisy estimate…
Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solu…