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Akshay Krishnamurthy
- > Home > Persons > Akshay Krishnamurthy
Publications
- 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) - [c72]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. ICML 2023: 30005-30021 - [i74]Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash:
Streaming Active Learning with Deep Neural Networks. CoRR abs/2303.02535 (2023) - 2022
- [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 - [c64]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR 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) - [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) - 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) - 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) - [c50]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR 2020 - [c48]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML 2020: 6961-6971 - [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 - [i45]Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins:
Efficient Contextual Bandits with Continuous Actions. CoRR abs/2006.06040 (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) - 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) - [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 - [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 - [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) - [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) - [i28]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR abs/1911.05815 (2019) - 2018
- [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 - [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) - [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
- [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 - [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) - 2016
- [c16]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
PAC Reinforcement Learning with Rich Observations. NIPS 2016: 1840-1848 - [i15]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR abs/1602.02722 (2016) - [i13]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. CoRR abs/1605.04812 (2016) - [i11]Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR abs/1610.09512 (2016) - 2015
- [c12]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better than Your Teacher. ICML 2015: 2058-2066 - [i10]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better Than Your Teacher. CoRR abs/1502.02206 (2015)
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