91 citations · 355 across the 18 of their papers we have counts for
6 papers · 1 filter
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Yining Wang, Ruosong Wang, Simon S. Du +1
We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity a…
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins +1
We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed ver…
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang +3
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specif…
Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of kno…
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider +1
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be p…
Detecting Activations over Graphs using Spanning Tree Wavelet Bases
James Sharpnack, Akshay Krishnamurthy, Aarti Singh
We consider the detection of activations over graphs under Gaussian noise, where signals are piece-wise constant over the graph. Despite the wide applicability of such a detection…