Akshay Krishnamurthy

Principal Researcher
Microsoft Research, New York City
New York, NY

Email: <my first name> at cs.umass.edu





I am a principal researcher at Microsoft Research, New York City. Previously, I spent two years as an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst and a year as a postdoctoral researcher at Microsoft Research, NYC. Before that, I completed my PhD in the Computer Science Department at Carnegie Mellon University, advised by Aarti Singh. I received my undergraduate degree in EECS at UC Berkeley.

My research interests are in machine learning and statistics. I am most excited about interactive learning, or learning settings that involve feedback-driven data collection. My recent interests revolve around decision making problems with limited feedback, including contextual bandits and reinforcement learning.

Microsoft research is hiring!
  • For full-time and postdoc positions in reinforcement learning, apply here.
  • For postdoc positions in machine learning at MSR NYC, apply here.

Selected Papers

Disagreement-based combinatorial pure exploration: Sample complexity bounds and an efficient algorithm.
Tongyi Cao and Akshay Krishnamurthy.
In Conference on Learning Theory, COLT 2019. [Arxiv version][poster]
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms.
Dylan J. Foster, Akshay Krishnamurthy.
In Neural Information Processing Systems, NeurIPS 2018. [Arxiv version][poster]
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable.
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.
In International Conference on Machine Learning, ICML 2017. [Arxiv version]
Low-Rank Matrix and Tensor Completion Via Adaptive Sampling.
Akshay Krishnamurthy and Aarti Singh.
In Neural Information Processing Systems, NIPS 2013. [Arxiv version]
Efficient Active Algorithms for Hierarchical Clustering.
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, and Aarti Singh.
In International Conference on Machine Learning, ICML 2012. [Arxiv version][code]