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2020 – today
- 2023
[c80]Alekh Agarwal, Yujia Jin, Tong Zhang:
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. COLT 2023: 987-1063- [c79]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. COLT 2023: 2114-2187 - [c78]Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. ICML 2023: 18733-18773 - [c77]Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans:
Stochastic Gradient Succeeds for Bandits. ICML 2023: 24325-24360 - [i75]Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR abs/2301.13857 (2023) - [i74]Alekh Agarwal, Claudio Gentile, Teodor V. Marinov:
Leveraging User-Triggered Supervision in Contextual Bandits. CoRR abs/2302.03784 (2023) - [i73]Alekh Agarwal, H. Brendan McMahan, Zheng Xu:
An Empirical Evaluation of Federated Contextual Bandit Algorithms. CoRR abs/2303.10218 (2023) - [i72]Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth:
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR abs/2305.17040 (2023) - [i71]Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, Nihar B. Shah:
Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments. CoRR abs/2311.09497 (2023) - [i70]Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova:
Efficient End-to-End Visual Document Understanding with Rationale Distillation. CoRR abs/2311.09612 (2023) - 2022
- [c76]Alekh Agarwal, Tong Zhang:
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. COLT 2022: 2704-2729 - [c75]Alekh Agarwal, Tong Zhang:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. COLT 2022: 2776-2814 - [c74]Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics. ICLR 2022 - [c73]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML 2022: 3852-3878 - [c72]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML 2022: 26517-26547 - [c71]Alekh Agarwal, Tong Zhang:
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. NeurIPS 2022 - [c70]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS 2022 - [i69]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR abs/2202.00063 (2022) - [i68]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR abs/2202.02446 (2022) - [i67]Alekh Agarwal, Tong Zhang:
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. CoRR abs/2202.05436 (2022) - [i66]Alekh Agarwal, Tong Zhang:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. CoRR abs/2203.08248 (2022) - [i65]Alekh Agarwal, Yuda Song
, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR abs/2205.14571 (2022) - [i64]Alekh Agarwal, Tong Zhang:
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. CoRR abs/2206.07659 (2022) - [i63]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) - [i62]Alekh Agarwal, Yujia Jin, Tong Zhang:
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. CoRR abs/2212.06069 (2022) - 2021
- [j12]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. 22: 98:1-98:76 (2021) - [j11]Alberto Bietti, Alekh Agarwal, John Langford:
A Contextual Bandit Bake-off. J. Mach. Learn. Res. 22: 133:1-133:49 (2021) - [c69]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. COLT 2021: 3681-3770 - [c68]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. COLT 2021: 4473-4525 - [c67]Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang:
Provably Correct Optimization and Exploration with Non-linear Policies. ICML 2021: 3263-3273 - [c66]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021: 6683-6694 - [i61]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) - [i60]Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett:
Towards a Dimension-Free Understanding of Adaptive Linear Control. CoRR abs/2103.10620 (2021) - [i59]Fei Feng, Wotao Yin, Alekh Agarwal, Lin F. Yang:
Provably Correct Optimization and Exploration with Non-linear Policies. CoRR abs/2103.11559 (2021) - [i58]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. CoRR abs/2103.12923 (2021) - [i57]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR abs/2106.06926 (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
- [c65]Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz:
Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations. AAAI 2020: 5207-5215 - [c64]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. COLT 2020: 64-66 - [c63]Alekh Agarwal, Sham M. Kakade, Lin F. Yang:
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal. COLT 2020: 67-83 - [c62]Chen-Yu Wei, Haipeng Luo, Alekh Agarwal:
Taking a hint: How to leverage loss predictors in contextual bandits? COLT 2020: 3583-3634 - [c61]Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal:
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR 2020 - [c60]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration. NeurIPS 2020 - [c59]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. NeurIPS 2020 - [c58]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. NeurIPS 2020 - [c57]Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement via Imitation of Multiple Oracles. NeurIPS 2020 - [c56]Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal:
Safe Reinforcement Learning via Curriculum Induction. NeurIPS 2020 - [i55]Chen-Yu Wei, Haipeng Luo, Alekh Agarwal:
Taking a hint: How to leverage loss predictors in contextual bandits? CoRR abs/2003.01922 (2020) - [i54]Alekh Agarwal, John Langford, Chen-Yu Wei:
Federated Residual Learning. CoRR abs/2003.12880 (2020) - [i53]Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan:
Reparameterized Variational Divergence Minimization for Stable Imitation. CoRR abs/2006.10810 (2020) - [i52]Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun:
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. CoRR abs/2006.10814 (2020) - [i51]Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal:
Safe Reinforcement Learning via Curriculum Induction. CoRR abs/2006.12136 (2020) - [i50]Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke, Ryen W. White:
Optimizing Interactive Systems via Data-Driven Objectives. CoRR abs/2006.12999 (2020) - [i49]Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement from Multiple Experts. CoRR abs/2007.00795 (2020) - [i48]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Provably Good Batch Reinforcement Learning Without Great Exploration. CoRR abs/2007.08202 (2020) - [i47]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. CoRR abs/2007.08459 (2020)
2010 – 2019
- 2019
- [j10]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) - [c55]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 - [c54]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting. DGS@ICLR 2019 - [c53]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. ICML 2019: 120-129 - [c52]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 - [c51]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. ICML 2019: 7335-7344 - [c50]Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. NeurIPS 2019: 11056-11068 - [c49]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with Stationary Distribution Correction. UAI 2019: 1180-1190 - [i46]Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. CoRR abs/1901.00301 (2019) - [i45]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) - [i44]Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill:
Off-Policy Policy Gradient with State Distribution Correction. CoRR abs/1904.08473 (2019) - [i43]Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz:
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations. CoRR abs/1905.05179 (2019) - [i42]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-based Algorithms. CoRR abs/1905.12843 (2019) - [i41]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) - [i40]Alekh Agarwal, Sham M. Kakade, Lin F. Yang:
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes. CoRR abs/1906.03804 (2019) - [i39]Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. CoRR abs/1906.09531 (2019) - [i38]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. CoRR abs/1908.00261 (2019) - 2018
- [c48]Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. COLT 2018: 1739-1776 - [c47]Nan Jiang, Alekh Agarwal:
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. COLT 2018: 3395-3398 - [c46]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. ICML 2018: 60-69 - [c45]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. ICML 2018: 1534-1543 - [c44]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. ICML 2018: 2923-2932 - [c43]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 - [i37]Alberto Bietti, Alekh Agarwal, John Langford:
Practical Evaluation and Optimization of Contextual Bandit Algorithms. CoRR abs/1802.04064 (2018) - [i36]Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III:
Hierarchical Imitation and Reinforcement Learning. CoRR abs/1803.00590 (2018) - [i35]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) - [i34]Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire:
Practical Contextual Bandits with Regression Oracles. CoRR abs/1803.01088 (2018) - [i33]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. CoRR abs/1803.02453 (2018) - [i32]Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR abs/1811.08540 (2018) - 2017
- [j9]Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli:
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory 63(1): 575-592 (2017) - [c42]Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire:
Open Problem: First-Order Regret Bounds for Contextual Bandits. COLT 2017: 4-7 - [c41]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. COLT 2017: 12-38 - [c40]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 - [c39]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. ICML 2017: 1915-1924 - [c38]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. ICML 2017: 3589-3597 - [c37]Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni:
Off-policy evaluation for slate recommendation. NIPS 2017: 3632-3642 - [i31]Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford:
Active Learning for Cost-Sensitive Classification. CoRR abs/1703.01014 (2017) - [i30]Haipeng Luo, Alekh Agarwal, John Langford:
Efficient Contextual Bandits in Non-stationary Worlds. CoRR abs/1708.01799 (2017) - 2016
- [j8]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c36]Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning by Sketching. NIPS 2016: 902-910 - [c35]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
PAC Reinforcement Learning with Rich Observations. NIPS 2016: 1840-1848 - [c34]Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Contextual semibandits via supervised learning oracles. NIPS 2016: 2388-2396 - [i29]Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning via Sketching. CoRR abs/1602.02202 (2016) - [i28]Akshay Krishnamurthy, Alekh Agarwal, John Langford:
Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations. CoRR abs/1602.02722 (2016) - [i27]David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire:
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. CoRR abs/1603.04119 (2016) - [i26]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) - [i25]Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, I. Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins:
A Multiworld Testing Decision Service. CoRR abs/1606.03966 (2016) - [i24]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) - [i23]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. CoRR abs/1612.01205 (2016) - [i22]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. CoRR abs/1612.06246 (2016) - 2015
- [c33]Alekh Agarwal, Léon Bottou:
A Lower Bound for the Optimization of Finite Sums. ICML 2015: 78-86 - [c32]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better than Your Teacher. ICML 2015: 2058-2066 - [c31]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. NIPS 2015: 2755-2763 - [c30]Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. NIPS 2015: 2989-2997 - [i21]Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford:
Learning to Search Better Than Your Teacher. CoRR abs/1502.02206 (2015) - [i20]Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík:
Efficient Contextual Semi-Bandit Learning. CoRR abs/1502.05890 (2015) - [i19]Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire:
Efficient and Parsimonious Agnostic Active Learning. CoRR abs/1506.08669 (2015) - [i18]Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. CoRR abs/1507.00407 (2015) - 2014
- [j7]Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford:
A reliable effective terascale linear learning system. J. Mach. Learn. Res. 15(1): 1111-1133 (2014) - [c29]Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions. CISS 2014: 1-2 - [c28]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries. COLT 2014: 123-137 - [c27]Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins:
Robust Multi-objective Learning with Mentor Feedback. COLT 2014: 726-741 - [c26]Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant:
Least Squares Revisited: Scalable Approaches for Multi-class Prediction. ICML 2014: 541-549 - [c25]Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire:
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML 2014: 1638-1646 - [c24]