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2020 – today
- 2023
[j7]Akshay Krishnamurthy
, Thodoris Lykouris, Chara Podimata, Robert E. Schapire:
Contextual Search in the Presence of Adversarial Corruptions. Oper. Res. 71(4): 1120-1135 (2023)- [j6]Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Rajiv Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. Trans. Mach. Learn. Res. 2023 (2023) - [c74]Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Learning Hidden Markov Models Using Conditional Samples. COLT 2023: 2014-2066 - [c73]Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using both offline and online data can make RL efficient. ICLR 2023 - [c72]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. ICLR 2023 - [c71]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. ICML 2023: 30005-30021 - [c70]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. ICML 2023: 31800-31851 - [i75]Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy:
Statistical Learning under Heterogenous Distribution Shift. CoRR abs/2302.13934 (2023) - [i74]Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang:
Learning Hidden Markov Models Using Conditional Samples. CoRR abs/2302.14753 (2023) - [i73]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. CoRR abs/2303.02535 (2023) - [i72]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Exposing Attention Glitches with Flip-Flop Language Modeling. CoRR abs/2306.00946 (2023) - [i71]Lequn Wang, Akshay Krishnamurthy, Aleksandrs Slivkins:
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits. CoRR abs/2306.07923 (2023) - 2022
- [c69]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra:
Investigating the Role of Negatives in Contrastive Representation Learning. AISTATS 2022: 7187-7209 - [c68]Aadirupa Saha, Akshay Krishnamurthy:
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. ALT 2022: 968-994 - [c67]Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu:
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. COLT 2022: 3489 - [c66]Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford:
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. COLT 2022: 5062-5127 - [c65]Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Anti-Concentrated Confidence Bonuses For Scalable Exploration. ICLR 2022 - [c64]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR 2022 - [c63]Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi:
Provable Reinforcement Learning with a Short-Term Memory. ICML 2022: 5832-5850 - [c62]Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Sparsity in Partially Controllable Linear Systems. ICML 2022: 5851-5860 - [c61]Vidya K. Muthukumar, Akshay Krishnamurthy:
Universal and data-adaptive algorithms for model selection in linear contextual bandits. ICML 2022: 16197-16222 - [c60]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML 2022: 19250-19286 - [c59]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS 2022 - [i70]Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi:
Provable Reinforcement Learning with a Short-Term Memory. CoRR abs/2202.03983 (2022) - [i69]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR abs/2202.14037 (2022) - [i68]Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, Sham M. Kakade:
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation. CoRR abs/2203.04236 (2022) - [i67]Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford:
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. CoRR abs/2206.04282 (2022) - [i66]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR abs/2206.10770 (2022) - [i65]Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan J. Foster, Lekan P. Molu, Rajan Chari, Akshay Krishnamurthy, John Langford:
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. CoRR abs/2207.08229 (2022) - [i64]Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun:
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient. CoRR abs/2210.06718 (2022) - [i63]Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang:
Transformers Learn Shortcuts to Automata. CoRR abs/2210.10749 (2022) - 2021
- [j5]Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu:
Contrastive Estimation Reveals Topic Posterior Information to Linear Models. J. Mach. Learn. Res. 22: 281:1-281:31 (2021) - [j4]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Trace Reconstruction: Generalized and Parameterized. IEEE Trans. Inf. Theory 67(6): 3233-3250 (2021) - [c58]Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu:
Contrastive learning, multi-view redundancy, and linear models. ALT 2021: 1179-1206 - [c57]Yining Wang, Ruosong Wang, Simon Shaolei Du, Akshay Krishnamurthy:
Optimism in Reinforcement Learning with Generalized Linear Function Approximation. ICLR 2021 - [c56]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Gone Fishing: Neural Active Learning with Fisher Embeddings. NeurIPS 2021: 8927-8939 - [c55]Dylan J. Foster, Akshay Krishnamurthy:
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. NeurIPS 2021: 18907-18919 - [c54]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS 2021: 26382-26394 - [c53]Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire:
Contextual search in the presence of irrational agents. STOC 2021: 910-918 - [i62]Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR abs/2102.07035 (2021) - [i61]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Gone Fishing: Neural Active Learning with Fisher Embeddings. CoRR abs/2106.09675 (2021) - [i60]Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra:
Investigating the Role of Negatives in Contrastive Representation Learning. CoRR abs/2106.09943 (2021) - [i59]Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire:
Bayesian decision-making under misspecified priors with applications to meta-learning. CoRR abs/2107.01509 (2021) - [i58]Dylan J. Foster, Akshay Krishnamurthy:
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination. CoRR abs/2107.02237 (2021) - [i57]Yonathan Efroni, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Sparsity in Partially Controllable Linear Systems. CoRR abs/2110.06150 (2021) - [i56]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics. CoRR abs/2110.08847 (2021) - [i55]Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade:
Anti-Concentrated Confidence Bonuses for Scalable Exploration. CoRR abs/2110.11202 (2021) - [i54]Vidya Muthukumar, Akshay Krishnamurthy:
Universal and data-adaptive algorithms for model selection in linear contextual bandits. CoRR abs/2111.04688 (2021) - [i53]Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu:
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. CoRR abs/2111.10919 (2021) - [i52]Aadirupa Saha, Akshay Krishnamurthy:
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability. CoRR abs/2111.12306 (2021) - 2020
- [j3]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21: 137:1-137:45 (2020) - [c52]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. ALT 2020: 468-489 - [c51]Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo:
Open Problem: Model Selection for Contextual Bandits. COLT 2020: 3842-3846 - [c50]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR 2020 - [c49]Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu:
Reward-Free Exploration for Reinforcement Learning. ICML 2020: 4870-4879 - [c48]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML 2020: 6961-6971 - [c47]Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík:
Doubly robust off-policy evaluation with shrinkage. ICML 2020: 9167-9176 - [c46]Yi Su, Pavithra Srinath, Akshay Krishnamurthy:
Adaptive Estimator Selection for Off-Policy Evaluation. ICML 2020: 9196-9205 - [c45]Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu:
Private Reinforcement Learning with PAC and Regret Guarantees. ICML 2020: 9754-9764 - [c44]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. NeurIPS 2020 - [c43]Tongyi Cao, Akshay Krishnamurthy:
Provably adaptive reinforcement learning in metric spaces. NeurIPS 2020 - [c42]Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu:
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. NeurIPS 2020 - [c41]Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun:
Information Theoretic Regret Bounds for Online Nonlinear Control. NeurIPS 2020 - [c40]Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. NeurIPS 2020 - [c39]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS 2020 - [i51]Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum:
Scalable Hierarchical Clustering with Tree Grafting. CoRR abs/2001.00076 (2020) - [i50]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. CoRR abs/2001.06776 (2020) - [i49]Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu:
Reward-Free Exploration for Reinforcement Learning. CoRR abs/2002.02794 (2020) - [i48]Yi Su, Pavithra Srinath, Akshay Krishnamurthy:
Adaptive Estimator Selection for Off-Policy Evaluation. CoRR abs/2002.07729 (2020) - [i47]Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata:
Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents. CoRR abs/2002.11650 (2020) - [i46]Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu:
Contrastive estimation reveals topic posterior information to linear models. CoRR abs/2003.02234 (2020) - [i45]Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. CoRR abs/2006.06040 (2020) - [i44]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. CoRR abs/2006.10814 (2020) - [i43]Tongyi Cao, Akshay Krishnamurthy:
Provably adaptive reinforcement learning in metric spaces. CoRR abs/2006.10875 (2020) - [i42]Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo:
Open Problem: Model Selection for Contextual Bandits. CoRR abs/2006.10940 (2020) - [i41]Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun:
Information Theoretic Regret Bounds for Online Nonlinear Control. CoRR abs/2006.12466 (2020) - [i40]Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu:
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. CoRR abs/2006.12484 (2020) - [i39]Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu:
Contrastive learning, multi-view redundancy, and linear models. CoRR abs/2008.10150 (2020) - [i38]Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu:
Private Reinforcement Learning with PAC and Regret Guarantees. CoRR abs/2009.09052 (2020) - [i37]Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford:
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR abs/2010.03799 (2020)
2010 – 2019
- 2019
- [j2]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. J. Mach. Learn. Res. 20: 65:1-65:50 (2019) - [c38]Tongyi Cao, Akshay Krishnamurthy:
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm. COLT 2019: 558-588 - [c37]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT 2019: 2025-2027 - [c36]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT 2019: 2898-2933 - [c35]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Trace Reconstruction: Generalized and Parameterized. ESA 2019: 68:1-68:25 - [c34]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. ICML 2019: 1665-1674 - [c33]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML 2019: 3222-3232 - [c32]Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum:
Scalable Hierarchical Clustering with Tree Grafting. KDD 2019: 1438-1448 - [c31]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Sample Complexity of Learning Mixture of Sparse Linear Regressions. NeurIPS 2019: 10531-10540 - [c30]Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo:
Model Selection for Contextual Bandits. NeurIPS 2019: 14714-14725 - [i36]Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. CoRR abs/1901.09018 (2019) - [i35]Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang:
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. CoRR abs/1902.01520 (2019) - [i34]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Trace Reconstruction: Generalized and Parameterized. CoRR abs/1904.09618 (2019) - [i33]Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo:
Model selection for contextual bandits. CoRR abs/1906.00531 (2019) - [i32]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR abs/1906.03671 (2019) - [i31]Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík:
Doubly robust off-policy evaluation with shrinkage. CoRR abs/1907.09623 (2019) - [i30]Xi Chen, Akshay Krishnamurthy, Yining Wang:
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers. CoRR abs/1910.04183 (2019) - [i29]Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Sample Complexity of Learning Mixtures of Sparse Linear Regressions. CoRR abs/1910.14106 (2019) - [i28]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR abs/1911.05815 (2019) - [i27]Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy:
Optimism in Reinforcement Learning with Generalized Linear Function Approximation. CoRR abs/1912.04136 (2019) - 2018
- [j1]Martin Azizyan, Akshay Krishnamurthy, Aarti Singh:
Extreme Compressive Sampling for Covariance Estimation. IEEE Trans. Inf. Theory 64(12): 7613-7635 (2018) - [c29]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS 2018: 133-142 - [c28]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. ICLR (Poster) 2018 - [c27]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. ICML 2018: 2781-2790 - [c26]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS 2018: 1429-1439 - [c25]Dylan J. Foster, Akshay Krishnamurthy:
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. NeurIPS 2018: 2626-2637 - [i26]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR abs/1803.00606 (2018) - [i25]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. CoRR abs/1803.04204 (2018) - [i24]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR abs/1805.09964 (2018) - [i23]Dylan J. Foster, Akshay Krishnamurthy:
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. CoRR abs/1806.10745 (2018) - [i22]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR abs/1811.08540 (2018) - 2017
- [c24]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. AKBC@NIPS 2017 - [c23]Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire:
Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT 2017: 4-7 - [c22]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML 2017: 1704-1713 - [c21]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. ICML 2017: 1915-1924 - [c20]Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum:
A Hierarchical Algorithm for Extreme Clustering. KDD 2017: 255-264 - [c19]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. NIPS 2017: 3632-3642 - [i21]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. CoRR abs/1703.01014 (2017) - [i20]Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum:
An Online Hierarchical Algorithm for Extreme Clustering. CoRR abs/1704.01858 (2017) - [i19]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos:
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR abs/1705.09236 (2017) - [i18]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alexander J. Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. CoRR abs/1711.05851 (2017) - [i17]Tongyi Cao, Akshay Krishnamurthy:
Disagreement-based combinatorial pure exploration: Efficient algorithms and an analysis with localization. CoRR abs/1711.08018 (2017) - 2016
- [c18]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. ICML 2016: 2159-2168 - [c17]Akshay Krishnamurthy:
Minimax structured normal means inference. ISIT 2016: 960-964 - [c16]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
PAC Reinforcement Learning with Rich Observations. NIPS 2016: 1840-1848 - [c15]Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Contextual semibandits via supervised learning oracles. NIPS 2016: 2388-2396 - [c14]Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. NIPS 2016: 3135-3143 - [i16]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. CoRR abs/1602.02454 (2016) - [i15]