Fine-grained privilege separation for web applications. - Akshay Krishnamurthy; Adrian Mettler; David A. Wagner 001 (WWW, 2010)

Noise Thresholds for Spectral Clustering. - Sivaraman Balakrishnan; Min Xu 010; Akshay Krishnamurthy; Aarti Singh (NIPS, 2011)

Efficient Active Algorithms for Hierarchical Clustering. - Akshay Krishnamurthy; Sivaraman Balakrishnan; Min Xu 010; Aarti Singh (ICML, 2012)

Robust multi-source network tomography using selective probes. - Akshay Krishnamurthy; Aarti Singh (INFOCOM, 2012)

Recovering graph-structured activations using adaptive compressive measurements. - Akshay Krishnamurthy; James Sharpnack; Aarti Singh (ACSSC, 2013)

Detecting Activations over Graphs using Spanning Tree Wavelet Bases. - James Sharpnack; Aarti Singh; Akshay Krishnamurthy (AISTATS, 2013)

Low-Rank Matrix and Tensor Completion via Adaptive Sampling. - Akshay Krishnamurthy; Aarti Singh (NIPS, 2013)

Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic. - James Sharpnack; Akshay Krishnamurthy; Aarti Singh (NIPS, 2013)

Subspace learning from extremely compressed measurements. - Martin Azizyan; Akshay Krishnamurthy; Aarti Singh (ACSSC, 2014)

Nonparametric Estimation of Renyi Divergence and Friends. - Akshay Krishnamurthy; Kirthevasan Kandasamy; Barnabás Póczos; Larry A. Wasserman (ICML, 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)

On the Power of Adaptivity in Matrix Completion and Approximation. - Akshay Krishnamurthy; Aarti Singh (CoRR, 2014)

On Estimating L22 Divergence. - Akshay Krishnamurthy; Kirthevasan Kandasamy; Barnabás Póczos; Larry A. Wasserman (AISTATS, 2015)

Learning to Search Better than Your Teacher. - Kai-Wei Chang; Akshay Krishnamurthy; Alekh Agarwal; Hal Daumé III; John Langford 001 (ICML, 2015)

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Barnabás Póczos; Larry A. Wasserman; James M. Robins (NIPS, 2015)

Minimaxity in Structured Normal Means Inference. - Akshay Krishnamurthy (CoRR, 2015)

Efficient Contextual Semi-Bandit Learning. - Akshay Krishnamurthy; Alekh Agarwal; Miroslav Dudík (CoRR, 2015)

Efficient Algorithms for Adversarial Contextual Learning. - Vasilis Syrgkanis; Akshay Krishnamurthy; Robert E. Schapire (ICML, 2016)

Minimax structured normal means inference. - Akshay Krishnamurthy (ISIT, 2016)

Contextual semibandits via supervised learning oracles. - Akshay Krishnamurthy; Alekh Agarwal; Miroslav Dudík (NIPS, 2016)

PAC Reinforcement Learning with Rich Observations. - Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (NIPS, 2016)

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. - Vasilis Syrgkanis; Haipeng Luo; Akshay Krishnamurthy; Robert E. Schapire (NIPS, 2016)

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. - David Abel; Alekh Agarwal; Fernando Diaz 001; Akshay Krishnamurthy; Robert E. Schapire (CoRR, 2016)

Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. - Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (CoRR, 2016)

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 (AKBC@NIPS, 2017)

Open Problem: First-Order Regret Bounds for Contextual Bandits. - Alekh Agarwal; Akshay Krishnamurthy; John Langford 001; Haipeng Luo; Robert E. Schapire (COLT, 2017)

Contextual Decision Processes with low Bellman rank are PAC-Learnable. - Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (ICML, 2017)

A Hierarchical Algorithm for Extreme Clustering. - Ari Kobren; Nicholas Monath; Akshay Krishnamurthy; Andrew McCallum (KDD, 2017)

Off-policy evaluation for slate recommendation. - Adith Swaminathan; Akshay Krishnamurthy; Alekh Agarwal; Miroslav Dudík; John Langford 001; Damien Jose; Imed Zitouni (NIPS, 2017)

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff G. Schneider; Barnabás Póczos (CoRR, 2017)

An Online Hierarchical Algorithm for Extreme Clustering. - Ari Kobren; Nicholas Monath; Akshay Krishnamurthy; Andrew McCallum (CoRR, 2017)

Disagreement-based combinatorial pure exploration: Efficient algorithms and an analysis with localization. - Tongyi Cao; Akshay Krishnamurthy (CoRR, 2017)

Parallelised Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (AISTATS, 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 (ICLR (Poster), 2018)

Semiparametric Contextual Bandits. - Akshay Krishnamurthy; Zhiwei Steven Wu; Vasilis Syrgkanis (ICML, 2018)

On Oracle-Efficient PAC RL with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (NeurIPS, 2018)

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. - Dylan J. Foster; Akshay Krishnamurthy (NeurIPS, 2018)

On Polynomial Time PAC Reinforcement Learning with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (CoRR, 2018)

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (CoRR, 2018)

Model-Based Reinforcement Learning in Contextual Decision Processes. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (CoRR, 2018)

Extreme Compressive Sampling for Covariance Estimation. - Martin Azizyan; Akshay Krishnamurthy; Aarti Singh (IEEE Trans. Inf. Theory, 2018)

Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm. - Tongyi Cao; Akshay Krishnamurthy (COLT, 2019)

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (COLT, 2019)

Provably efficient RL with Rich Observations via Latent State Decoding. - Simon S. Du; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal; Miroslav Dudík; John Langford 001 (ICML, 2019)

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (ICML, 2019)

Scalable Hierarchical Clustering with Tree Grafting. - Nicholas Monath; Ari Kobren; Akshay Krishnamurthy; Michael R. Glass; Andrew McCallum (KDD, 2019)

Model Selection for Contextual Bandits. - Dylan J. Foster; Akshay Krishnamurthy; Haipeng Luo (NeurIPS, 2019)

Sample Complexity of Learning Mixture of Sparse Linear Regressions. - Akshay Krishnamurthy; Arya Mazumdar; Andrew McGregor 001; Soumyabrata Pal (NeurIPS, 2019)

Sample Complexity of Learning Mixtures of Sparse Linear Regressions. - Akshay Krishnamurthy; Arya Mazumdar; Andrew McGregor 001; Soumyabrata Pal (CoRR, 2019)

Active Learning for Cost-Sensitive Classification. - Akshay Krishnamurthy; Alekh Agarwal; Tzu-Kuo Huang; Hal Daumé III; John Langford 001 (J. Mach. Learn. Res., 2019)

Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. - Akshay Krishnamurthy; Arya Mazumdar; Andrew McGregor 001; Soumyabrata Pal (ALT, 2020)

Open Problem: Model Selection for Contextual Bandits. - Dylan J. Foster; Akshay Krishnamurthy; Haipeng Luo (COLT, 2020)

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. - Jordan T. Ash; Chicheng Zhang; Akshay Krishnamurthy; John Langford 001; Alekh Agarwal (ICLR, 2020)

Reward-Free Exploration for Reinforcement Learning. - Chi Jin 001; Akshay Krishnamurthy; Max Simchowitz; Tiancheng Yu (ICML, 2020)

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. - Dipendra Misra; Mikael Henaff; Akshay Krishnamurthy; John Langford 001 (ICML, 2020)

Doubly robust off-policy evaluation with shrinkage. - Yi Su; Maria Dimakopoulou; Akshay Krishnamurthy; Miroslav Dudík (ICML, 2020)

Adaptive Estimator Selection for Off-Policy Evaluation. - Yi Su; Pavithra Srinath; Akshay Krishnamurthy (ICML, 2020)

Private Reinforcement Learning with PAC and Regret Guarantees. - Giuseppe Vietri; Borja Balle; Akshay Krishnamurthy; Zhiwei Steven Wu (ICML, 2020)

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. - Alekh Agarwal; Sham M. Kakade; Akshay Krishnamurthy; Wen Sun 002 (NeurIPS, 2020)

Provably adaptive reinforcement learning in metric spaces. - Tongyi Cao; Akshay Krishnamurthy (NeurIPS, 2020)

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. - Chi Jin 001; Sham M. Kakade; Akshay Krishnamurthy; Qinghua Liu (NeurIPS, 2020)

Information Theoretic Regret Bounds for Online Nonlinear Control. - Sham M. Kakade; Akshay Krishnamurthy; Kendall Lowrey; Motoya Ohnishi; Wen Sun 002 (NeurIPS, 2020)

Efficient Contextual Bandits with Continuous Actions. - Maryam Majzoubi; Chicheng Zhang; Rajan Chari; Akshay Krishnamurthy; John Langford 001; Aleksandrs Slivkins (NeurIPS, 2020)

Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zakaria Mhammedi; Dylan J. Foster; Max Simchowitz; Dipendra Misra; Wen Sun 002; Akshay Krishnamurthy; Alexander Rakhlin; John Langford 001 (NeurIPS, 2020)

Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents. - Akshay Krishnamurthy; Thodoris Lykouris; Chara Podimata (CoRR, 2020)

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. - Akshay Krishnamurthy; John Langford 001; Aleksandrs Slivkins; Chicheng Zhang (J. Mach. Learn. Res., 2020)

Contrastive learning, multi-view redundancy, and linear models. - Christopher Tosh; Akshay Krishnamurthy; Daniel Hsu 001 (ALT, 2021)

Optimism in Reinforcement Learning with Generalized Linear Function Approximation. - Yining Wang 001; Ruosong Wang; Simon Shaolei Du; Akshay Krishnamurthy (ICLR, 2021)

Gone Fishing: Neural Active Learning with Fisher Embeddings. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (NeurIPS, 2021)

Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. - Dylan J. Foster; Akshay Krishnamurthy (NeurIPS, 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 (NeurIPS, 2021)

Contextual search in the presence of irrational agents. - Akshay Krishnamurthy; Thodoris Lykouris; Chara Podimata; Robert E. Schapire (STOC, 2021)

Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. - Yonathan Efroni; Dipendra Misra; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (CoRR, 2021)

Contrastive Estimation Reveals Topic Posterior Information to Linear Models. - Christopher Tosh; Akshay Krishnamurthy; Daniel Hsu 001 (J. Mach. Learn. Res., 2021)

Trace Reconstruction: Generalized and Parameterized. - Akshay Krishnamurthy; Arya Mazumdar; Andrew McGregor 001; Soumyabrata Pal (IEEE Trans. Inf. Theory, 2021)

Investigating the Role of Negatives in Contrastive Representation Learning. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Dipendra Misra (AISTATS, 2022)

Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. - Aadirupa Saha; Akshay Krishnamurthy (ALT, 2022)

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. - Yonathan Efroni; Dylan J. Foster; Dipendra Misra; Akshay Krishnamurthy; John Langford 001 (COLT, 2022)

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. - Dylan J. Foster; Akshay Krishnamurthy; David Simchi-Levi; Yunzong Xu (COLT, 2022)

Anti-Concentrated Confidence Bonuses For Scalable Exploration. - Jordan T. Ash; Cyril Zhang; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (ICLR, 2022)

Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. - Yonathan Efroni; Dipendra Misra; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (ICLR, 2022)

Provable Reinforcement Learning with a Short-Term Memory. - Yonathan Efroni; Chi Jin 001; Akshay Krishnamurthy; Sobhan Miryoosefi (ICML, 2022)

Sparsity in Partially Controllable Linear Systems. - Yonathan Efroni; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (ICML, 2022)

Universal and data-adaptive algorithms for model selection in linear contextual bandits. - Vidya K. Muthukumar; Akshay Krishnamurthy (ICML, 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 (ICML, 2022)

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. - Jinglin Chen; Aditya Modi 002; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (NeurIPS, 2022)

A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. - Juan C. Perdomo; Akshay Krishnamurthy; Peter L. Bartlett; Sham M. Kakade (CoRR, 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 001 (CoRR, 2022)

Learning Hidden Markov Models Using Conditional Samples. - Gaurav Mahajan; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (COLT, 2023)

Hybrid RL: Using both offline and online data can make RL efficient. - Yuda Song 001; Yifei Zhou; Ayush Sekhari; Drew Bagnell; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2023)

Transformers Learn Shortcuts to Automata. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (ICLR, 2023)

Streaming Active Learning with Deep Neural Networks. - Akanksha Saran; Safoora Yousefi; Akshay Krishnamurthy; John Langford 001; Jordan T. Ash (ICML, 2023)

Statistical Learning under Heterogenous Distribution Shift. - Max Simchowitz; Anurag Ajay; Pulkit Agrawal 001; Akshay Krishnamurthy (ICML, 2023)

Exposing Attention Glitches with Flip-Flop Language Modeling. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (NeurIPS, 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)

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 001 (Trans. Mach. Learn. Res., 2023)

Contextual Search in the Presence of Adversarial Corruptions. - Akshay Krishnamurthy; Thodoris Lykouris; Chara Podimata; Robert E. Schapire (Oper. Res., 2023)

Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits. - Lequn Wang; Akshay Krishnamurthy; Alex Slivkins (AISTATS, 2024)

Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. - Philip Amortila; Tongyi Cao; Akshay Krishnamurthy (COLT, 2024)

Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression. - Adam Block; Dylan J. Foster; Akshay Krishnamurthy; Max Simchowitz; Cyril Zhang (ICLR, 2024)

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics. - Yuda Song 001; Lili Wu; Dylan J. Foster; Akshay Krishnamurthy (ICML, 2024)

Scalable Online Exploration via Coverability. - Philip Amortila; Dylan J. Foster; Akshay Krishnamurthy (ICML, 2024)

Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Akshay Krishnamurthy; Zakaria Mhammedi (NeurIPS, 2024)

Can large language models explore in-context? - Akshay Krishnamurthy; Keegan Harris; Dylan J. Foster; Cyril Zhang; Aleksandrs Slivkins (NeurIPS, 2024)

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers. - Xi Chen 010; Akshay Krishnamurthy; Yining Wang 001 (Oper. Res., 2024)

Model-Free Representation Learning and Exploration in Low-Rank MDPs. - Aditya Modi 002; Jinglin Chen; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (J. Mach. Learn. Res., 2024)

The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). - Akshay Krishnamurthy; Gene Li; Ayush Sekhari (COLT, 2025)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (COLT, 2025)

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 (ICLR, 2025)

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 002; Akshay Krishnamurthy; Dylan J. Foster (ICLR, 2025)

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. - Runzhe Wu; Ayush Sekhari; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2025)

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. - Tengyang Xie; Dylan J. Foster; Akshay Krishnamurthy; Corby Rosset; Ahmed Hassan Awadallah; Alexander Rakhlin (ICLR, 2025)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification. - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (CoRR, 2025)

Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. - Audrey Huang; Adam Block; Qinghua Liu; Nan Jiang 008; Dylan J. Foster; Akshay Krishnamurthy (CoRR, 2025)

The Role of Environment Access in Agnostic Reinforcement Learning. - Akshay Krishnamurthy; Gene Li; Ayush Sekhari (CoRR, 2025)