Akshay Krishnamurthy

Assistant Professor
College of Information and Computer Sciences
University of Massachusetts, Amherst

Office: CS 258
Email: <my first name> at cs.umass.edu





I am an assistant professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. Previously, I spent a year as a postdoctoral researcher at Microsoft Research, New York City. 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 specifically interested in interactive learning and learning settings involving feedback-driven data collection. My thesis focuses on interactive approaches for discovering and exploiting low-dimensional structure in data. More recently, I have been studying complex decision making problems with limited feedback, which fall under the umbrella of reinforcement learning.


Fall 2016: CS 691E: Interactive Machine Learning: Algorithms and Theory
Fall 2016: CS 311: Introduction to Algorithms (with Andrew McGregor)

Selected Papers

PAC Reinforcement Learning from Rich Observations.
Akshay Krishnamurthy, Alekh Agarwal, John Langford.
To appear in Neural Information Processing Systems, NIPS 2016. [Arxiv version]
Efficient Algorithms for Adversarial Contextual Learning.
Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire.
In International Conference on Machine Learning, ICML 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]