Akshay Krishnamurthy

According to our database, Akshay Krishnamurthy authored at least 109 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Model-Free Representation Learning and Exploration in Low-Rank MDPs.

Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

J. Mach. Learn. Res., 2024

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers.

Xi Chen, Akshay Krishnamurthy, Yining Wang

Oper. Res., 2024

Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity.

Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi

CoRR, 2024

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization.

Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster

CoRR, 2024

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics.

Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun

CoRR, 2024

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.

Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin

CoRR, 2024

Can large language models explore in-context?

Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins

CoRR, 2024

Scalable Online Exploration via Coverability.

Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics.

Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression.

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz, Cyril Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning.

Philip Amortila, Tongyi Cao, Akshay Krishnamurthy

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits.

Lequn Wang, Akshay Krishnamurthy, Alex Slivkins

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Contextual Search in the Presence of Adversarial Corruptions.

Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire

Oper. Res., July, 2023

Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models.

Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

Trans. Mach. Learn. Res., 2023

A Complete Characterization of Linear Estimators for Offline Policy Evaluation.

Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade

J. Mach. Learn. Res., 2023

Exposing Attention Glitches with Flip-Flop Language Modeling.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Statistical Learning under Heterogenous Distribution Shift.

Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy

Proceedings of the International Conference on Machine Learning, 2023

Streaming Active Learning with Deep Neural Networks.

Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash

Proceedings of the International Conference on Machine Learning, 2023

Transformers Learn Shortcuts to Automata.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Hybrid RL: Using both offline and online data can make RL efficient.

Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning Hidden Markov Models Using Conditional Samples.

Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models.

Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford

CoRR, 2022

A Sharp Characterization of Linear Estimators for Offline Policy Evaluation.

Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade

CoRR, 2022

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL.

Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding Contrastive Learning Requires Incorporating Inductive Biases.

Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy

Proceedings of the International Conference on Machine Learning, 2022

Universal and data-adaptive algorithms for model selection in linear contextual bandits.

Vidya K. Muthukumar, Akshay Krishnamurthy

Proceedings of the International Conference on Machine Learning, 2022

Sparsity in Partially Controllable Linear Systems.

Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the International Conference on Machine Learning, 2022

Provable Reinforcement Learning with a Short-Term Memory.

Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

Proceedings of the International Conference on Machine Learning, 2022

Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics.

Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Tenth International Conference on Learning Representations, 2022

Anti-Concentrated Confidence Bonuses For Scalable Exploration.

Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Tenth International Conference on Learning Representations, 2022

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation.

Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information.

Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability.

Aadirupa Saha, Akshay Krishnamurthy

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Investigating the Role of Negatives in Contrastive Representation Learning.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Trace Reconstruction: Generalized and Parameterized.

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

IEEE Trans. Inf. Theory, 2021

Contrastive Estimation Reveals Topic Posterior Information to Linear Models.

Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

J. Mach. Learn. Res., 2021

Provable RL with Exogenous Distractors via Multistep Inverse Dynamics.

Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2021

Contextual search in the presence of irrational agents.

Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Bayesian decision-making under misspecified priors with applications to meta-learning.

Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination.

Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Gone Fishing: Neural Active Learning with Fisher Embeddings.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimism in Reinforcement Learning with Generalized Linear Function Approximation.

Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy

Proceedings of the 9th International Conference on Learning Representations, 2021

Contrastive learning, multi-view redundancy, and linear models.

Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

Proceedings of the Algorithmic Learning Theory, 2021

2020

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting.

Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

J. Mach. Learn. Res., 2020

Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents.

Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata

CoRR, 2020

Learning the Linear Quadratic Regulator from Nonlinear Observations.

Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Contextual Bandits with Continuous Actions.

Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Information Theoretic Regret Bounds for Online Nonlinear Control.

Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs.

Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably adaptive reinforcement learning in metric spaces.

Tongyi Cao, Akshay Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs.

Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Private Reinforcement Learning with PAC and Regret Guarantees.

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Adaptive Estimator Selection for Off-Policy Evaluation.

Yi Su, Pavithra Srinath, Akshay Krishnamurthy

Proceedings of the 37th International Conference on Machine Learning, 2020

Doubly robust off-policy evaluation with shrinkage.

Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík

Proceedings of the 37th International Conference on Machine Learning, 2020

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning.

Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford

Proceedings of the 37th International Conference on Machine Learning, 2020

Reward-Free Exploration for Reinforcement Learning.

Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal

Proceedings of the 8th International Conference on Learning Representations, 2020

Open Problem: Model Selection for Contextual Bandits.

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

Proceedings of the Conference on Learning Theory, 2020

Algebraic and Analytic Approaches for Parameter Learning in Mixture Models.

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

Proceedings of the Algorithmic Learning Theory, 2020

2019

Active Learning for Cost-Sensitive Classification.

Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford

J. Mach. Learn. Res., 2019

Sample Complexity of Learning Mixtures of Sparse Linear Regressions.

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

CoRR, 2019

Sample Complexity of Learning Mixture of Sparse Linear Regressions.

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Model Selection for Contextual Bandits.

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Hierarchical Clustering with Tree Grafting.

Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the 36th International Conference on Machine Learning, 2019

Provably efficient RL with Rich Observations via Latent State Decoding.

Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford

Proceedings of the 36th International Conference on Machine Learning, 2019

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Conference on Learning Theory, 2019

Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm.

Tongyi Cao, Akshay Krishnamurthy

Proceedings of the Conference on Learning Theory, 2019

2018

Extreme Compressive Sampling for Covariance Estimation.

Martin Azizyan, Akshay Krishnamurthy, Aarti Singh

IEEE Trans. Inf. Theory, 2018

Model-Based Reinforcement Learning in Contextual Decision Processes.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2018

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

CoRR, 2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

CoRR, 2018

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms.

Dylan J. Foster, Akshay Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Oracle-Efficient PAC RL with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Semiparametric Contextual Bandits.

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

Parallelised Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Disagreement-based combinatorial pure exploration: Efficient algorithms and an analysis with localization.

Tongyi Cao, Akshay Krishnamurthy

CoRR, 2017

An Online Hierarchical Algorithm for Extreme Clustering.

Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum

CoRR, 2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos

CoRR, 2017

Off-policy evaluation for slate recommendation.

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Hierarchical Algorithm for Extreme Clustering.

Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Contextual Decision Processes with low Bellman rank are PAC-Learnable.

Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the 34th International Conference on Machine Learning, 2017

Open Problem: First-Order Regret Bounds for Contextual Bandits.

Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire

Proceedings of the 30th Conference on Learning Theory, 2017

Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning.

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

Proceedings of the 6th Workshop on Automated Knowledge Base Construction, 2017

2016

Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations.

Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains.

David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire

CoRR, 2016

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits.

Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

PAC Reinforcement Learning with Rich Observations.

Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Contextual semibandits via supervised learning oracles.

Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Minimax structured normal means inference.

Akshay Krishnamurthy

Proceedings of the IEEE International Symposium on Information Theory, 2016

Efficient Algorithms for Adversarial Contextual Learning.

Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Efficient Contextual Semi-Bandit Learning.

Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík

CoRR, 2015

Minimaxity in Structured Normal Means Inference.

Akshay Krishnamurthy

CoRR, 2015

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Learning to Search Better than Your Teacher.

Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford

Proceedings of the 32nd International Conference on Machine Learning, 2015

On Estimating L22 Divergence.

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

On the Power of Adaptivity in Matrix Completion and Approximation.

Akshay Krishnamurthy, Aarti Singh

CoRR, 2014

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins

CoRR, 2014

Nonparametric Estimation of Renyi Divergence and Friends.

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

Proceedings of the 31th International Conference on Machine Learning, 2014

Subspace learning from extremely compressed measurements.

Martin Azizyan, Akshay Krishnamurthy, Aarti Singh

Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic.

James Sharpnack, Akshay Krishnamurthy, Aarti Singh

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Low-Rank Matrix and Tensor Completion via Adaptive Sampling.

Akshay Krishnamurthy, Aarti Singh

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Detecting Activations over Graphs using Spanning Tree Wavelet Bases.

James Sharpnack, Aarti Singh, Akshay Krishnamurthy

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Recovering graph-structured activations using adaptive compressive measurements.

Akshay Krishnamurthy, James Sharpnack, Aarti Singh

Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012

Robust multi-source network tomography using selective probes.

Akshay Krishnamurthy, Aarti Singh

Proceedings of the IEEE INFOCOM 2012, Orlando, FL, USA, March 25-30, 2012, 2012

Efficient Active Algorithms for Hierarchical Clustering.

Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, Aarti Singh

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Noise Thresholds for Spectral Clustering.

Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Fine-grained privilege separation for web applications.

Akshay Krishnamurthy, Adrian Mettler, David A. Wagner

Proceedings of the 19th International Conference on World Wide Web, 2010