Alekh Agarwal

According to our database, Alekh Agarwal authored at least 130 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Model-Free Representation Learning and Exploration in Low-Rank MDPs.

Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

J. Mach. Learn. Res., 2024

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar

CoRR, 2024

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning.

Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent

CoRR, 2024

Robust Preference Optimization through Reward Model Distillation.

Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal, Ahmad Beirami, Chirag Nagpal, Petet Shaw, Jonathan Berant

CoRR, 2024

Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization.

Teodor V. Marinov, Alekh Agarwal, Mircea Trofin

CoRR, 2024

Theoretical guarantees on the best-of-n alignment policy.

Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh

CoRR, 2024

Efficient End-to-End Visual Document Understanding with Rationale Distillation.

Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning.

Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Minimaximalist Approach to Reinforcement Learning from Human Feedback.

Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

The Non-linear F-Design and Applications to Interactive Learning.

Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning.

Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks.

Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth

Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.

Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D'Amour, Dj Dvijotham, Adam Fisch, Katherine A. Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant

CoRR, 2023

Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments.

Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, Nihar B. Shah

CoRR, 2023

An Empirical Evaluation of Federated Contextual Bandit Algorithms.

Alekh Agarwal, H. Brendan McMahan, Zheng Xu

CoRR, 2023

Leveraging User-Triggered Supervision in Contextual Bandits.

Alekh Agarwal, Claudio Gentile, Teodor V. Marinov

CoRR, 2023

Ordering-based Conditions for Global Convergence of Policy Gradient Methods.

Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Gradient Succeeds for Bandits.

Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans

Proceedings of the International Conference on Machine Learning, 2023

Learning in POMDPs is Sample-Efficient with Hindsight Observability.

Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang

Proceedings of the International Conference on Machine Learning, 2023

VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation.

Alekh Agarwal, Yujia Jin, Tong Zhang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Provable Benefits of Representational Transfer in Reinforcement Learning.

Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

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

Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity.

Alekh Agarwal, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach.

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun

Proceedings of the International Conference on Machine Learning, 2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning.

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal

Proceedings of the International Conference on Machine Learning, 2022

Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics.

Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Tenth International Conference on Learning Representations, 2022

Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling.

Alekh Agarwal, Tong Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Minimax Regret Optimization for Robust Machine Learning under Distribution Shift.

Alekh Agarwal, Tong Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

A Contextual Bandit Bake-off.

Alberto Bietti, Alekh Agarwal, John Langford

J. Mach. Learn. Res., 2021

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift.

Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

J. Mach. Learn. Res., 2021

Provable RL with Exogenous Distractors via Multistep Inverse Dynamics.

Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2021

Bellman-consistent Pessimism for Offline Reinforcement Learning.

Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provably Correct Optimization and Exploration with Non-linear Policies.

Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang

Proceedings of the 38th International Conference on Machine Learning, 2021

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation.

Andrea Zanette, Ching-An Cheng, Alekh Agarwal

Proceedings of the Conference on Learning Theory, 2021

Towards a Dimension-Free Understanding of Adaptive Linear Control.

Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett

Proceedings of the Conference on Learning Theory, 2021

2020

Provably Good Batch Reinforcement Learning Without Great Exploration.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

CoRR, 2020

Policy Improvement from Multiple Experts.

Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

CoRR, 2020

Optimizing Interactive Systems via Data-Driven Objectives.

Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke, Ryen W. White

CoRR, 2020

Reparameterized Variational Divergence Minimization for Stable Imitation.

Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan

CoRR, 2020

Federated Residual Learning.

Alekh Agarwal, John Langford, Chen-Yu Wei

CoRR, 2020

Safe Reinforcement Learning via Curriculum Induction.

Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Policy Improvement via Imitation of Multiple Oracles.

Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs.

Alekh Agarwal, Sham M. Kakade, Akshay Krishnamurthy, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning.

Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal

Proceedings of the 8th International Conference on Learning Representations, 2020

Taking a hint: How to leverage loss predictors in contextual bandits?

Chen-Yu Wei, Haipeng Luo, Alekh Agarwal

Proceedings of the Conference on Learning Theory, 2020

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal.

Alekh Agarwal, Sham M. Kakade, Lin F. Yang

Proceedings of the Conference on Learning Theory, 2020

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes.

Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

Proceedings of the Conference on Learning Theory, 2020

Metareasoning in Modular Software Systems: On-the-Fly Configuration Using Reinforcement Learning with Rich Contextual Representations.

Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Active Learning for Cost-Sensitive Classification.

Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, John Langford

J. Mach. Learn. Res., 2019

On the Optimality of Sparse Model-Based Planning for Markov Decision Processes.

Alekh Agarwal, Sham M. Kakade, Lin F. Yang

CoRR, 2019

Off-Policy Policy Gradient with State Distribution Correction.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

CoRR, 2019

Off-Policy Policy Gradient with Stationary Distribution Correction.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting.

Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.

Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban

Proceedings of the 36th International Conference on Machine Learning, 2019

Provably efficient RL with Rich Observations via Latent State Decoding.

Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford

Proceedings of the 36th International Conference on Machine Learning, 2019

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms.

Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

Proceedings of the 36th International Conference on Machine Learning, 2019

Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting.

Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

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

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Conference on Learning Theory, 2019

2018

Model-Based Reinforcement Learning in Contextual Decision Processes.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

CoRR, 2018

Practical Evaluation and Optimization of Contextual Bandit Algorithms.

Alberto Bietti, Alekh Agarwal, John Langford

CoRR, 2018

On Oracle-Efficient PAC RL with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Practical Contextual Bandits with Regression Oracles.

Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire

Proceedings of the 35th International Conference on Machine Learning, 2018

A Reductions Approach to Fair Classification.

Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach

Proceedings of the 35th International Conference on Machine Learning, 2018

Hierarchical Imitation and Reinforcement Learning.

Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III

Proceedings of the 35th International Conference on Machine Learning, 2018

Efficient Contextual Bandits in Non-stationary Worlds.

Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford

Proceedings of the Conference On Learning Theory, 2018

Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon.

Nan Jiang, Alekh Agarwal

Proceedings of the Conference On Learning Theory, 2018

2017

A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

IEEE Trans. Inf. Theory, 2017

Efficient Contextual Bandits in Non-stationary Worlds.

Haipeng Luo, Alekh Agarwal, John Langford

CoRR, 2017

Off-policy evaluation for slate recommendation.

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík, John Langford, Damien Jose, Imed Zitouni

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits.

Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík

Proceedings of the 34th International Conference on Machine Learning, 2017

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

Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the 34th International Conference on Machine Learning, 2017

Corralling a Band of Bandit Algorithms.

Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire

Proceedings of the 30th Conference on Learning Theory, 2017

Open Problem: First-Order Regret Bounds for Contextual Bandits.

Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli

SIAM J. Optim., 2016

Efficient Second Order Online Learning via Sketching.

Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford

CoRR, 2016

Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations.

Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2016

A Multiworld Testing Decision Service.

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

CoRR, 2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains.

David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire

CoRR, 2016

Efficient Second Order Online Learning by Sketching.

Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

PAC Reinforcement Learning with Rich Observations.

Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Contextual semibandits via supervised learning oracles.

Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Efficient Contextual Semi-Bandit Learning.

Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík

CoRR, 2015

Fast Convergence of Regularized Learning in Games.

Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Efficient and Parsimonious Agnostic Active Learning.

Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Learning to Search Better than Your Teacher.

Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, John Langford

Proceedings of the 32nd International Conference on Machine Learning, 2015

A Lower Bound for the Optimization of Finite Sums.

Alekh Agarwal, Léon Bottou

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A reliable effective terascale linear learning system.

Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford

J. Mach. Learn. Res., 2014

Scalable Nonlinear Learning with Adaptive Polynomial Expansions.

Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky

CoRR, 2014

Scalable Non-linear Learning with Adaptive Polynomial Expansions.

Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Least Squares Revisited: Scalable Approaches for Multi-class Prediction.

Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant

Proceedings of the 31th International Conference on Machine Learning, 2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits.

Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire

Proceedings of the 31th International Conference on Machine Learning, 2014

Robust Multi-objective Learning with Mentor Feedback.

Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins

Proceedings of The 27th Conference on Learning Theory, 2014

Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

Proceedings of The 27th Conference on Learning Theory, 2014

Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions.

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

Proceedings of the 48th Annual Conference on Information Sciences and Systems, 2014

2013

The Generalization Ability of Online Algorithms for Dependent Data.

Alekh Agarwal, John C. Duchi

IEEE Trans. Inf. Theory, 2013

Stochastic Convex Optimization with Bandit Feedback.

Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin

SIAM J. Optim., 2013

Para-active learning.

Alekh Agarwal, Léon Bottou, Miroslav Dudík, John Langford

CoRR, 2013

Exact Recovery of Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

CoRR, 2013

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

CoRR, 2013

Selective sampling algorithms for cost-sensitive multiclass prediction.

Alekh Agarwal

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Computational Trade-offs in Statistical Learning.

Alekh Agarwal

PhD thesis, 2012

Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization.

Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright

IEEE Trans. Inf. Theory, 2012

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

IEEE Trans. Autom. Control., 2012

Ergodic Mirror Descent.

John C. Duchi, Alekh Agarwal, Mikael Johansson, Michael I. Jordan

SIAM J. Optim., 2012

Contextual Bandit Learning with Predictable Rewards.

Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Oracle inequalities for computationally adaptive model selection

Alekh Agarwal, Peter L. Bartlett, John C. Duchi

CoRR, 2012

FASt global convergence of gradient methods for solving regularized M-estimation.

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions.

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Dual averaging for distributed optimization.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011

Oracle inequalities for computationally budgeted model selection.

Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clément Levrard

Proceedings of the COLT 2011, 2011

Fast global convergence of gradient methods for high-dimensional statistical recovery

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

CoRR, 2011

Online and Batch Learning Algorithms for Data with Missing Features

Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett

CoRR, 2011

Learning with Missing Features.

Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett

Proceedings of the UAI 2011, 2011

Distributed Delayed Stochastic Optimization.

Alekh Agarwal, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions.

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes.

Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright

J. Mach. Learn. Res., 2010

Optimal Allocation Strategies for the Dark Pool Problem.

Alekh Agarwal, Peter L. Bartlett, Max Dama

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Distributed Dual Averaging In Networks.

John C. Duchi, Alekh Agarwal, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Fast global convergence rates of gradient methods for high-dimensional statistical recovery.

Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.

Alekh Agarwal, Ofer Dekel, Lin Xiao

Proceedings of the COLT 2010, 2010

2009

Information-theoretic lower bounds on the oracle complexity of convex optimization.

Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, Martin J. Wainwright

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

A Stochastic View of Optimal Regret through Minimax Duality.

Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin

Proceedings of the COLT 2009, 2009

2008

Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes.

Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright

Proceedings of the Machine Learning, 2008

2007

An Analysis of Inference with the Universum.

Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Learning random walks to rank nodes in graphs.

Alekh Agarwal, Soumen Chakrabarti

Proceedings of the Machine Learning, 2007

2006

Learning Parameters in Entity Relationship Graphs from Ranking Preferences.

Soumen Chakrabarti, Alekh Agarwal

Proceedings of the Knowledge Discovery in Databases: PKDD 2006, 2006

Learning to rank networked entities.

Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006