114 citations · 348 across the 55 of their papers we have counts for
7 papers · 1 filter
The Fragility of Optimized Bandit Algorithms
Lin Fan, Peter W. Glynn
Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that designs that are optimal over certain exponential f…
Distributed stochastic optimization with large delays
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos +2
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms…
Distributionally Robust Martingale Optimal Transport
Zhengqing Zhou, Jose Blanchet, Peter W. Glynn
We study the problem of bounding path-dependent expectations (within any finite time horizon ) over the class of discrete-time martingales whose marginal distributions lie withi…
Unbiased Optimal Stopping via the MUSE
Zhengqing Zhou, Guanyang Wang, Jose Blanchet +1
We propose a new unbiased estimator for estimating the utility of the optimal stopping problem. The MUSE, short for Multilevel Unbiased Stopping Estimator, constructs the unbiased…
Frank-Wolfe Methods in Probability Space
Carson Kent, Jose Blanchet, Peter Glynn
We introduce a new class of Frank-Wolfe algorithms for minimizing differentiable functionals over probability measures. This framework can be shown to encompass a diverse range of…
Diffusion Approximations for Thompson Sampling in the Small Gap Regime
Lin Fan, Peter W. Glynn
We study the process-level dynamics of Thompson sampling and related sampling-based bandit algorithms in the ``small gap'' regime, where the gaps between the arm means are of order…