A. Krishnamurthy

发表

Efficient Active Algorithms for Hierarchical Clustering pdf

Sivaraman Balakrishnan, Akshay Krishnamurthy, Min Xu, 2012, ICML.

On Estimating $L_2^2$ Divergence

L. Wasserman, Kirthevasan Kandasamy, B. Póczos, 2014, AISTATS 2015.

On Estimating L 22 Divergence

A. Krishnamurthy, 2015 .

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning pdf

Alexander J. Smola, Akshay Krishnamurthy, Rajarshi Das, 2017, ICLR.

Off-policy evaluation for slate recommendation

John Langford, Akshay Krishnamurthy, Miroslav Dudík, 2016, NIPS.

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

John Langford, Akshay Krishnamurthy, Aleksandrs Slivkins, 2019, COLT.

Efficient Contextual Bandits with Continuous Actions

J. Langford, A. Krishnamurthy, Aleksandrs Slivkins, 2020, NeurIPS.

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

James M. Robins, Kirthevasan Kandasamy, Akshay Krishnamurthy, 2014, ArXiv.

Noise Thresholds for Spectral Clustering

Sivaraman Balakrishnan, Akshay Krishnamurthy, Min Xu, 2011, NIPS.

Detecting Activations over Graphs using Spanning Tree Wavelet Bases

Akshay Krishnamurthy, Aarti Singh, James Sharpnack, 2012, AISTATS.

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

S. Kakade, Chi Jin, A. Krishnamurthy, 2020, NeurIPS.

On Estimating L22 Divergence

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2015, AISTATS.

Title : Efficient Online Bayesian Hierarchical Clustering

A. Krishnamurthy, 2018 .

Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction

M. Zaheer, Alex Smola, R. Das, 2019 .

Recovering Euclidean Distance Matrices via Landmark MDS

A. Krishnamurthy, 2011 .

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

Akshay Krishnamurthy, Rajarshi Das, Andrew McCallum, 2017, AKBC@NIPS.

Contextual Search in the Presence of Adversarial Corruptions

R. Schapire, A. Krishnamurthy, Thodoris Lykouris, 2020, 2002.11650.

Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information

Dylan J. Foster, Dipendra Kumar Misra, J. Langford, 2022, COLT.

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

Akshay Krishnamurthy, Robert E. Schapire, Max Simchowitz, 2021, NeurIPS.

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

A. Krishnamurthy, Vidya Muthukumar, 2021, ArXiv.

Sparsity in Partially Controllable Linear Systems

S. Kakade, A. Krishnamurthy, Cyril Zhang, 2021, ArXiv.

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

Yining Wang, A. Krishnamurthy, Xi Chen, 2019, ArXiv.

Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation

Dylan J. Foster, A. Krishnamurthy, D. Simchi-Levi, 2021, COLT.

MULTISTEP INVERSE DYNAMICS

Dipendra Kumar Misra, J. Langford, A. Krishnamurthy, 2022 .

Transformers Learn Shortcuts to Automata

A. Krishnamurthy, J. Ash, Cyril Zhang, 2022, ArXiv.

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

J. Bagnell, A. Krishnamurthy, Ayush Sekhari, 2022, ArXiv.

Contextual Search in the Presence of Adversarial Corruptions

R. Schapire, A. Krishnamurthy, Thodoris Lykouris, 2020, Operational Research.

Model-free Representation Learning and Exploration in Low-rank MDPs

A. Krishnamurthy, Alekh Agarwal, Nan Jiang, 2021, ArXiv.

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

A. Krishnamurthy, Alekh Agarwal, Nan Jiang, 2022, NeurIPS.

Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability pdf

Akshay Krishnamurthy, Aadirupa Saha, A. Krishnamurthy, 2021, ALT.

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

Akshay Krishnamurthy, Dipendra Misra, Mikael Henaff, 2019, ICML.

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

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, ArXiv.

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

John Langford, Alekh Agarwal, Jordan T. Ash, 2019, ICLR.

Contextual search in the presence of irrational agents

Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, 2020, STOC.

Learning to Search Better than Your Teacher pdf

John Langford, Akshay Krishnamurthy, Kai-Wei Chang, 2015, ICML.

Nonparametric Estimation of Renyi Divergence and Friends

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2014, ICML.

Semiparametric Contextual Bandits pdf

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis, 2018, ICML.

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

Nan Jiang, John Langford, Akshay Krishnamurthy, 2016, ICML.

Optimism in Reinforcement Learning with Generalized Linear Function Approximation pdf

Ruosong Wang, Akshay Krishnamurthy, Yining Wang, 2019, ICLR.

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

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, COLT.

On Oracle-Efficient PAC RL with Rich Observations

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, NeurIPS.

PAC Reinforcement Learning with Rich Observations

John Langford, Akshay Krishnamurthy, Alekh Agarwal, 2016, NIPS.

Provably efficient RL with Rich Observations via Latent State Decoding pdf

Nan Jiang, John Langford, Akshay Krishnamurthy, 2019, ICML.

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

Dylan J. Foster, Dipendra Kumar Misra, J. Langford, 2022, Trans. Mach. Learn. Res..

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs

S. Kakade, A. Krishnamurthy, Alekh Agarwal, 2020, NeurIPS.

Information Theoretic Regret Bounds for Online Nonlinear Control

S. Kakade, Kendall Lowrey, A. Krishnamurthy, 2020, NeurIPS.

Statistical Learning under Heterogenous Distribution Shift

Max Simchowitz, Pulkit Agrawal, A. Krishnamurthy, 2023, ArXiv.

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Dipendra Kumar Misra, S. Kakade, A. Krishnamurthy, 2022, ICML.

Investigating the Role of Negatives in Contrastive Representation Learning pdf

Akshay Krishnamurthy, Jordan T. Ash, Dipendra Kumar Misra, 2021, AISTATS.

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

Akshay Krishnamurthy, Christopher Tosh, Daniel J. Hsu, 2020, ALT.

Contrastive estimation reveals topic posterior information to linear models pdf

Akshay Krishnamurthy, Christopher Tosh, Daniel J. Hsu, 2020, J. Mach. Learn. Res..

Parallelised Bayesian Optimisation via Thompson Sampling

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, AISTATS.

Transformers Learn Shortcuts to Automata

A. Krishnamurthy, J. Ash, Cyril Zhang, 2022, ICLR.

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2019, ICML.

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

J. Bagnell, A. Krishnamurthy, Ayush Sekhari, 2022, ICLR.

Gone Fishing: Neural Active Learning with Fisher Embeddings pdf

Akshay Krishnamurthy, Jordan T. Ash, Sham Kakade, 2021, NeurIPS.

Streaming Active Learning with Deep Neural Networks

J. Langford, A. Krishnamurthy, J. Ash, 2023, ArXiv.

Adaptive Estimator Selection for Off-Policy Evaluation pdf

Yi Su, Akshay Krishnamurthy, Pavithra Srinath, 2020, ICML.

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations

James M. Robins, Kirthevasan Kandasamy, Akshay Krishnamurthy, 2015, NIPS.

A Hierarchical Algorithm for Extreme Clustering

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2017, KDD.

Exposing Attention Glitches with Flip-Flop Language Modeling

A. Krishnamurthy, J. Ash, Cyril Zhang, 2023, ArXiv.

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits

Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire, 2016, NIPS.

Streaming Active Learning with Deep Neural Networks

J. Langford, A. Krishnamurthy, J. Ash, 2023, ICML.

Provable Reinforcement Learning with a Short-Term Memory

Chi Jin, A. Krishnamurthy, Yonathan Efroni, 2022, ICML.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling pdf

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2017, ArXiv.

Model selection for contextual bandits

Haipeng Luo, Akshay Krishnamurthy, Dylan J. Foster, 2019, NeurIPS.

Scalable Hierarchical Clustering with Tree Grafting

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2019, KDD.

Active Learning for Cost-Sensitive Classification

John Langford, Akshay Krishnamurthy, Tzu-Kuo Huang, 2017, ICML.

Doubly robust off-policy evaluation with shrinkage pdf

Yi Su, Akshay Krishnamurthy, Miroslav Dudík, 2019, ICML.

Learning the Linear Quadratic Regulator from Nonlinear Observations pdf

Akshay Krishnamurthy, Max Simchowitz, Dipendra Misra, 2020, NeurIPS.

Reward-Free Exploration for Reinforcement Learning

Akshay Krishnamurthy, Chi Jin, Max Simchowitz, 2020, ICML.

Anti-Concentrated Confidence Bonuses for Scalable Exploration

S. Kakade, A. Krishnamurthy, J. Ash, 2021, ICLR.

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

Akshay Krishnamurthy, Dylan J. Foster, A. Krishnamurthy, 2021, NeurIPS.

A Sharp Characterization of Linear Estimators for Offline Policy Evaluation

Juan C. Perdomo, S. Kakade, P. Bartlett, 2022, ArXiv.

GENERALIZED LINEAR FUNCTION APPROXIMATION

Yining Wang, A. Krishnamurthy, Ruosong Wang, 2021 .

Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits

A. Krishnamurthy, Aleksandrs Slivkins, Lequn Wang, 2023, arXiv.org.

High-Dimensional Clustering with Sparse Gaussian Mixture Models

Akshay Krishnamurthy, A. Krishnamurthy, 2010 .

Sample Complexity of Learning Mixtures of Sparse Linear Regressions

Soumyabrata Pal, Akshay Krishnamurthy, Andrew McGregor, 2019, NeurIPS.

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

Akshay Krishnamurthy, Tongyi Cao, A. Krishnamurthy, 2017, COLT.

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

Akshay Krishnamurthy, Tongyi Cao, A. Krishnamurthy, 2017, ArXiv.

Provably adaptive reinforcement learning in metric spaces

A. Krishnamurthy, Tongyi Cao, 2020, NeurIPS.

Open Problem: First-Order Regret Bounds for Contextual Bandits

John Langford, Haipeng Luo, Akshay Krishnamurthy, 2017, COLT.

Private Reinforcement Learning with PAC and Regret Guarantees pdf

Akshay Krishnamurthy, Zhiwei Steven Wu, Borja Balle, 2020, ICML.

Efficient Contextual Bandits with Continuous Actions pdf

Akshay Krishnamurthy, Aleksandrs Slivkins, Chicheng Zhang, 2020, NeurIPS.

An Online Hierarchical Algorithm for Extreme Clustering pdf

Akshay Krishnamurthy, Andrew McCallum, Ari Kobren, 2017, ArXiv.

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

Akshay Krishnamurthy, Aarti Singh, James Sharpnack, 2013, NIPS.

Extreme Compressive Sampling for Covariance Estimation

Aarti Singh, Akshay Krishnamurthy, Martin Azizyan, 2015, IEEE Transactions on Information Theory.

Low-Rank Matrix and Tensor Completion via Adaptive Sampling

Akshay Krishnamurthy, Aarti Singh, Aarti Singh, 2013, NIPS.

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

J. Langford, A. Krishnamurthy, Alekh Agarwal, 2018, COLT.

Subspace learning from extremely compressed measurements

Akshay Krishnamurthy, Aarti Singh, Martin Azizyan, 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics

Dipendra Kumar Misra, J. Langford, A. Krishnamurthy, 2022, International Conference on Learning Representations.

Interactive Algorithms for Unsupervised Machine Learning

Akshay Krishnamurthy, A. Krishnamurthy, 2015 .

Minimax structured normal means inference

Akshay Krishnamurthy, A. Krishnamurthy, 2015, 2016 IEEE International Symposium on Information Theory (ISIT).

On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018 .

On Polynomial Time PAC Reinforcement Learning with Rich Observations pdf

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, ArXiv.

Algebraic and Analytic Approaches for Parameter Learning in Mixture Models pdf

Akshay Krishnamurthy, Soumyabrata Pal, Andrew McGregor, 2020, ALT.

Contextual semibandits via supervised learning oracles

Akshay Krishnamurthy, Miroslav Dudík, Alekh Agarwal, 2015, NIPS.

Efficient Contextual Semi-Bandit Learning pdf

Akshay Krishnamurthy, Miroslav Dudík, Alekh Agarwal, 2015, ArXiv.

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains

R. Schapire, A. Krishnamurthy, Alekh Agarwal, 2016, ArXiv.

Contextual bandits with surrogate losses: Margin bounds and efficient algorithms

Akshay Krishnamurthy, Dylan J. Foster, A. Krishnamurthy, 2018, NeurIPS.

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

Akshay Krishnamurthy, Christopher Tosh, Daniel J. Hsu, 2020, ALT.

Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits

A. Krishnamurthy, Aleksandrs Slivkins, Lequn Wang, 2023, ArXiv.

Exposing Attention Glitches with Flip-Flop Language Modeling

A. Krishnamurthy, J. Ash, Cyril Zhang, 2023, NeurIPS.

Learning to Search Better than Your Teacher

J. Langford, A. Krishnamurthy, Alekh Agarwal, 2015, ICML.

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

Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, 2020, ArXiv.

Adaptive Estimator Selection for Off-Policy Evaluation

A. Krishnamurthy, Yi-Hsun Su, Pavithra Srinath, 2020, ICML.

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

Dylan J. Foster, Dipendra Kumar Misra, J. Langford, 2022, arXiv.org.