Akshay Krishnamurthy

Researcher
Microsoft Research, New York City
New York, NY

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

Main

Publications

Teaching

Miscellaneous


New Preprints

Myopic bayesian design of experiments via posterior sampling and probabilistic programming.
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos.
[Arxiv version]
Disagreement-based Combinatorial Pure Exploration: Efficient Algorithms and an Analysis with Localization.
Tongyi Cao, Akshay Krishnamurthy.
[Arxiv version]
Active Learning for Cost-Sensitive Classification.
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daume III, John Langford.
Preliminary version in International Conference on Machine Learning, ICML 2017.
Extended version available on Arxiv.

Publications

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms.
Dylan J. Foster, Akshay Krishnamurthy.
To appear in Neural Information Processing Systems, NIPS 2018. [Arxiv version]
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations.
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.
To appear in Neural Information Processing Systems, NIPS 2018. [Arxiv version]
Extreme Compressive Sampling for Covariance Estimation.
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh.
To appear in IEEE Transactions on Information Theory, 2018. [Arxiv version]
Semiparametric Contextual Bandits.
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis.
In International Conference on Machine Learning, ICML 2018. [Arxiv version]
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum.
In International Conference on Learning Representations, ICLR 2018. [Arxiv version]
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling.
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos.
In Artificial Intelligence and Statistics, AISTATS 2018. Oral presentation. [Arxiv version]`
Off-policy Evaluation for Slate Recommendation.
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudik, John Langford, Damien Jose, Imed Zitouni.
In Neural Information Processing Systems, NIPS 2017. Oral presentation. [Arxiv version]
An Online Hierarchical Algorithm for Extreme Clustering.
Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum.
In Knowledge Discovery and Data Mining, KDD 2017. [Arxiv version]
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]
Open Problem: First-Order Regret Bounds for Contextual Bandits.
Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire.
In Conference on Learning Theory, COLT 2017
PAC Reinforcement Learning from Rich Observations.
Akshay Krishnamurthy, Alekh Agarwal, John Langford.
In Neural Information Processing Systems, NIPS 2016. [Arxiv version]
Contextual Semibandits via Supervised Learning Oracles.
Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudik.
In Neural Information Processing Systems, NIPS 2016 [Arxiv version][code]
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits.
Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire.
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]
Nonparametric von Mises Estimators for Entropies, Divergences, and Mutual Informations.
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, and James M. Robins.
In Neural Information Processing Systems, NIPS 2015. [Arxiv version][code]
Learning to search better than your teacher.
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume III, John Langford.
International Conference on Machine Learning, ICML 2015. [Arxiv version]
On Estimating L_2^2 Divergence.
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos and Larry Wasserman.
In Artificial Intelligence and Statistics, AISTATS 2015. [Arxiv version][code]
Subspace Learning from Extremely Compressed Measurements.
Akshay Krishnamurthy, Martin Azizyan, and Aarti Singh.
In Asilomar Conference on Signals, Systems and Computers, 2014. [Arxiv version]
Nonparametric Estimation of Renyi Divergence and Friends.
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, and Larry Wasserman.
In International Conference on Machine Learning, ICML 2014. [Arxiv version][code]
Recovering Graph-Structured Activations using Adaptive Compressive Measurements.
Akshay Krishnamurthy, James Sharpnack, and Aarti Singh.
In Asilomar Conference on Signals, Systems and Computers, 2013. [Arxiv version]
Winner of the Best Student Paper Award.
Low-Rank Matrix and Tensor Completion Via Adaptive Sampling.
Akshay Krishnamurthy and Aarti Singh.
In Neural Information Processing Systems, NIPS 2013. [Arxiv version]
Near-Optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic.
James Sharpnack, Akshay Krishnamurthy, and Aarti Singh.
In Neural Information Processing Systems, NIPS 2013. [Arxiv version]
Detecting Activations over Graphs using Spanning Tree Wavelet Bases.
James Sharpnack, Akshay Krishnamurthy and Aarti Singh.
In Artificial Intelligence and Statistics, AISTATS 2013. Oral presentation. [Arxiv version]
Completion of high-rank ultrametric matrices using selective entries.
Aarti Singh, Akshay Krishnamurthy, Sivaraman Balakrishnan and Min Xu.
In International Conference on Signal Processing and Communications, SPCOM 2012. [pdf]
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]
Robust Multi-Source Network Tomography using Selective Probes.
Akshay Krishnamurthy and Aarti Singh.
In International Conference on Computer Communication, INFOCOM 2012. [pdf]
Noise Thresholds for Spectral Clustering.
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh.
In Neural Information Processing Systems, NIPS 2011. Spotlight presentation. [pdf]
DEGAS: De novo discovery of dysregulated pathways in human diseases.
Igor Ulitsky, Akshay Krishnamurthy, Richard Karp, Ron Shamir.
In PLoS ONE. October 2010. [pdf]
Fine-Grained Privilege Separation for Web Applications.
Akshay Krishnamurthy, Adrian Mettler, and David Wagner.
In International World Wide Web Conference, WWW 2010. [pdf]

Old Preprints

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains.
David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire.
[Arxiv version]
On the Power of Adaptivity in Matrix Completion and Approximation.
Akshay Krishnamurthy and Aarti Singh.
[Arxiv version]

PhD Thesis

Interactive Algorithms for Unsupervised Machine Learning. [pdf][Proposal].