Akshay Krishnamurthy

Researcher
Microsoft Research, New York City
New York, NY

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

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Miscellaneous


I am a 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.


Selected Papers

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]
PAC Reinforcement Learning from Rich Observations.
Akshay Krishnamurthy, Alekh Agarwal, John Langford.
In Neural Information Processing Systems, NIPS 2016. [Arxiv version]
Minimax Structured Normal Means Inference.
Akshay Krishnamurthy.
In International Symposium on Information Theory, ISIT 2016. [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]