Hiteshi Sharma

Generating author description...

All published works

Action	Title	Year	Authors
+ PDF Chat	Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning	2024	Nabil Omi Hosein Hasanbeig Hiteshi Sharma Sriram K. Rajamani Siddhartha Sen
+ PDF Chat	Self-Exploring Language Models: Active Preference Elicitation for Online Alignment	2024	Shenao Zhang Donghan Yu Hiteshi Sharma Ziyi Yang Shuohang Wang Hany Hassan Zhaoran Wang
+	Fine-Tuning Language Models with Advantage-Induced Policy Alignment	2023	Banghua Zhu Hiteshi Sharma Felipe Vieira Frujeri Shi Dong Chenguang Zhu Michael I. Jordan Jiantao Jiao
+	ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning	2023	Hosein Hasanbeig Hiteshi Sharma Leo Betthauser Felipe Vieira Frujeri Ida Momennejad
+	Evaluating Cognitive Maps and Planning in Large Language Models with CogEval	2023	Ida Momennejad Hosein Hasanbeig Felipe do Nascimento Vieira Hiteshi Sharma Robert Osazuwa Ness Nebojša Jojić Hamid Palangi Jonathan Larson
+	Language Models can be Logical Solvers	2023	Jiazhan Feng Ruochen Xu Junheng Hao Hiteshi Sharma Yelong Shen Dongyan Zhao Weizhu Chen
+	Randomized Policy Learning for Continuous State and Action MDPs	2020	Hiteshi Sharma Rahul Jain
+	An Empirical Relative Value Learning Algorithm for Non-parametric MDPs with Continuous State Space	2019	Hiteshi Sharma Rahul Jain Abhishek Gupta
+	A Universal Empirical Dynamic Programming Algorithm for Continuous State MDPs	2019	William B. Haskell Rahul Jain Hiteshi Sharma P. L. Yu
+	Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes	2019	Chen-Yu Wei Mehdi Jafarnia-Jahromi Haipeng Luo Hiteshi Sharma Rahul Jain
+	An Empirical Dynamic Programming Algorithm for Continuous MDPs	2017	William B. Haskell Rahul Jain Hiteshi Sharma P. L. Yu
+	A dynamical systems framework for stochastic iterative optimization	2016	William B. Haskell Rahul Jain Hiteshi Sharma

Common Coauthors

Coauthor	Papers Together
Rahul Jain	6
William B. Haskell	3
Hosein Hasanbeig	3
Felipe Vieira Frujeri	2
P. L. Yu	2
Ida Momennejad	2
Jiazhan Feng	1
Banghua Zhu	1
Leo Betthauser	1
Hamid Palangi	1
Jiantao Jiao	1
Chenguang Zhu	1
Shuohang Wang	1
Chen-Yu Wei	1
Nabil Omi	1
Dongyan Zhao	1
Siddhartha Sen	1
Michael I. Jordan	1
Robert Osazuwa Ness	1
Weizhu Chen	1
Shi Dong	1
Ziyi Yang	1
Jonathan Larson	1
Sriram K. Rajamani	1
Mehdi Jafarnia-Jahromi	1
Felipe do Nascimento Vieira	1
Haipeng Luo	1
Yelong Shen	1
Zhaoran Wang	1
Donghan Yu	1
Ruochen Xu	1
Nebojša Jojić	1
Shenao Zhang	1
Hany Hassan	1
Junheng Hao	1
Abhishek Gupta	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Empirical Dynamic Programming	2016	William B. Haskell Rahul Jain Dileep Kalathil	5
+	Finite-Time Bounds for Fitted Value Iteration	2008	Rémi Munos Csaba Szepesvári	4
+ PDF Chat	Uniform approximation of functions with random bases	2008	Ali Rahimi Benjamin Recht	3
+	Using Randomization to Break the Curse of Dimensionality	1997	John Rust	2
+	Nonlinear approximation	1998	Ronald DeVore	2
+ PDF Chat	Shannon sampling II: Connections to learning theory	2005	Steve Smale Ding‐Xuan Zhou	2
+	Modelling transition dynamics in MDPs with RKHS embeddings	2012	Steffen Grünewälder Guy Lever Luca Baldassarre Massi Pontil Arthur Gretton	2
+	Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension	1995	David Haussler	2
+ PDF Chat	Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization	2014	Shai Shalev‐Shwartz Tong Zhang	1
+ PDF Chat	Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm	2015	Deanna Needell Nathan Srebro Rachel Ward	1
+	Parallelized Stochastic Gradient Descent	2010	Martin Zinkevich Markus Weimer Lihong Li Alex Smola	1
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	1
+	An empirical algorithm for relative value iteration for average-cost MDPs	2015	Abhishek Gupta Rahul Jain Peter W. Glynn	1
+	Minimax Regret Bounds for Reinforcement Learning	2017	Mohammad Gheshlaghi Azar Ian Osband Rémi Munos	1
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	1
+	Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control	2017	Riashat Islam Peter Henderson Maziar Gomrokchi Doina Precup	1
+	Learning-based Control of Unknown Linear Systems with Thompson Sampling	2017	Yi Ouyang Mukul Gagrani Rahul Jain	1
+	An Empirical Dynamic Programming Algorithm for Continuous MDPs	2017	William B. Haskell Rahul Jain Hiteshi Sharma P. L. Yu	1
+	Primal-Dual $π$ Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems	2017	Mengdi Wang	1
+	Simple random search provides a competitive approach to reinforcement learning	2018	Horia Mania Aurelia Guy Benjamin Recht	1
+	Regret Bounds for Reinforcement Learning via Markov Chain Concentration	2018	Ronald Ortner	1
+	Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP	2019	Kefan Dong Yuanhao Wang Xiaoyu Chen Liwei Wang	1
+	Improved Path-length Regret Bounds for Bandits	2019	Sébastien Bubeck Yuanzhi Li Haipeng Luo Chen-Yu Wei	1
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	1
+	Fast Stochastic Alternating Direction Method of Multipliers	2013	Leon Wenliang Zhong James T. Kwok	1
+	Is Q-learning Provably Efficient?	2018	Chi Jin Zeyuan Allen-Zhu Sébastien Bubeck Michael I. Jordan	1
+	Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning	2018	Ronan Fruit Matteo Pirotta Alessandro Lazaric Ronald Ortner	1
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu	1
+ PDF Chat	Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints	2016	Laurent Lessard Benjamin Recht Andrew Packard	1
+	Exploration-Enhanced POLITEX	2019	Yasin Abbasi-Yadkori Nevena Lazic Csaba Szepesvári Gellért Weisz	1
+	Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function	2019	Zihan Zhang Xiangyang Ji	1
+	Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs	2018	Mohammad Sadegh Talebi Odalric-Ambrym Maillard	1
+ PDF Chat	Deep Randomized Neural Networks	2020	Claudio Gallicchio Simone Scardapane	1
+	Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers	2014	Taiji Suzuki	1
+	Generalization and Exploration via Randomized Value Functions	2014	Ian Osband Benjamin Van Roy Zheng Wen	1
+	Convex Optimization	2004	Stephen Boyd Lieven Vandenberghe	1
+	REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs	2012	Peter L. Bartlett Ambuj Tewari	1
+	Proximal Stochastic Dual Coordinate Ascent	2012	Shai Shalev‐Shwartz Tong Zhang	1
+	Modelling transition dynamics in MDPs with RKHS embeddings	2012	Guy Lever Luca Baldassarre Arthur Gretton Massimiliano Pontil Steffen Gr new lder	1
+ PDF Chat	Incremental Stochastic Subgradient Algorithms for Convex Optimization	2009	S. Sundhar Ram A. Nedić Venugopal V. Veeravalli	1
+ PDF Chat	A Proximal Stochastic Gradient Method with Progressive Variance Reduction	2014	Lin Xiao Tong Zhang	1
+	Convergence analysis of gradient descent stochastic algorithms	1996	Alexander Shapiro Y. Wardi	1
+	The uniform convergence of nearest neighbor regression function estimators and their application in optimization	1978	Luc Devroye	1