Akshay Krishnamurthy

I am a senior principal research manager 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

Self-improvement in language models: The sharpening mechanism.
Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy.
To appear in International Conference on Learning Representations, ICLR 2025. Oral presentation
[Arxiv version]

Transformers learn shortcuts to automata.
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang.
In International Conference on Learning Representations, ICLR 2023. Oral presentation
[Arxiv version]

Efficient first order contextual bandits: Prediction, allocation, and triangular discrimination.
Dylan J. Foster, Akshay Krishnamurthy.
In Neural Information Processing Systems, NeurIPS 2021. Oral presentation.
[Arxiv version]

FLAMBE: Structural complexity and representation learning of low rank MDPs.
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun.
In Neural Information Processing Systems, NeurIPS 2020. Oral presentation.
[Arxiv version][poster]

Kinematic state abstraction and provably efficient rich-observation reinforcement learning.
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford.
In International Conference on Machine Learning, ICML 2020.
[Arxiv version][blog]

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 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, NeurIPS 2013.
[Arxiv version]