758 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Microsoft (United States)US53 papers
- Cornell UniversityUS15 papers
- Carnegie Mellon UniversityUS9 papers
- University of Massachusetts AmherstUS8 papers
- Princeton UniversityUS7 papers
- Columbia UniversityUS6 papers
- Microsoft Research (United Kingdom)GB6 papers
- University of PennsylvaniaUS6 papers
- University of Maryland, College ParkUS5 papers
- University of MichiganUS5 papers
- Harvard UniversityUS4 papers
- Stanford UniversityUS4 papers
6 papers · 1 filter
A Contextual Bandit Bake-off
Alberto Bietti, Alekh Agarwal, John Langford
Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally…
Markov Decision Processes with Continuous Side Information
Aditya Modi, Nan Jiang, Satinder Singh +1
We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-info…
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm
Tongyi Cao, Akshay Krishnamurthy
We design new algorithms for the combinatorial pure exploration problem in the multi-arm bandit framework. In this problem, we are given distributions and a collection of subse…
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik
We study the off-policy evaluation problem---estimating the value of a target policy using data collected by another policy---under the contextual bandit model. We consider the gen…
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
Aaron Schein, Mingyuan Zhou, David M. Blei +1
We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country to…
A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal, Leon Bottou
This paper presents a lower bound for optimizing a finite sum of functions, where each function is -smooth and the sum is -strongly convex. We show that no algorithm can…