Lalit Jain - RankMe

50 papers A* 16A 5C 1Misc 1Journal 26Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accelerating Scientific Research with Gemini: Case Studies and Common Techniques. CoRR David P. Woodruff, Vincent Cohen-Addad, Lalit Jain, Jieming Mao, Song Zuo, MohammadHossein Bateni, Simina Brânzei, Michael P. Brenner, Lin Chen, Ying Feng, Lance Fortnow, Gang Fu, Ziyi Guan, Zahra Hadizadeh, Mohammad T. Hajiaghayi, Mahdi JafariRaviz, Adel Javanmard, Karthik C. S., Ken-ichi Kawarabayashi, Ravi Kumar, Silvio Lattanzi, Euiwoong Lee, Yi Li, Ioannis Panageas, Dimitris Paparas, Benjamin Przybocki, Bernardo Subercaseaux, Ola Svensson, Shayan Taherijam, Xuan Wu, Eylon Yogev, Morteza Zadimoghaddam, Samson Zhou, Yossi Matias, James Manyika, Vahab Mirrokni
2025	A*	`conf`	An Empirical Risk Minimization Approach for Offline Inverse Reinforcement Learning and Dynamic Discrete Choice Models. EC Enoch Hyunwook Kang, Hema Yoganarasimhan, Lalit Jain
2025	J	`jnl`	Gradients can train reward models: An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model. CoRR Enoch H. Kang, Hema Yoganarasimhan, Lalit Jain
2025	J	`jnl`	Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve. Mark. Sci. Ian N. Weaver, Vineet Kumar, Lalit Jain
2024	A	`conf`	A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. AISTATS Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin Jamieson
2024	A*	`conf`	Adaptive Experimentation When You Can't Experiment. NeurIPS Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
2024	J	`jnl`	Adaptive Experimentation When You Can't Experiment. CoRR Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
2024	A*	`conf`	Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. WWW Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain
2024	J	`jnl`	Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. CoRR Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain
2024	J	`jnl`	Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models. Mark. Sci. Lalit Jain, Zhaoqi Li, Erfan Loghmani, Blake Mason, Hema Yoganarasimhan
2024	A	`conf`	Fair Active Learning in Low-Data Regimes. UAI Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2024	A*	`conf`	Nearly Minimax Optimal Submodular Maximization with Bandit Feedback. NeurIPS Artin Tajdini, Lalit Jain, Kevin Jamieson
2024	J	`jnl`	Off-Policy Evaluation from Logged Human Feedback. CoRR Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee
2024	A	`conf`	Optimal Exploration is no harder than Thompson Sampling. AISTATS Zhaoqi Li, Kevin Jamieson, Lalit Jain
2024	A	`conf`	Pessimistic Off-Policy Multi-Objective Optimization. AISTATS Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
2023	J	`jnl`	A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. CoRR Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin Jamieson
2023	A*	`conf`	Experimental Designs for Heteroskedastic Variance. NeurIPS Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber, Blake Mason, Houssam Nassif, Lalit Jain
2023	J	`jnl`	Fair Active Learning in Low-Data Regimes. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2023	J	`jnl`	Minimax Optimal Submodular Optimization with Bandit Feedback. CoRR Artin Tajdini, Lalit Jain, Kevin Jamieson
2023	J	`jnl`	Optimal Exploration is no harder than Thompson Sampling. CoRR Zhaoqi Li, Kevin Jamieson, Lalit Jain
2023	J	`jnl`	Pessimistic Off-Policy Multi-Objective Optimization. CoRR Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
2022	A*	`conf`	Active Learning with Safety Constraints. NeurIPS Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin Jamieson
2022	J	`jnl`	Active Learning with Safety Constraints. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2022	J	`jnl`	Adaptive Experimental Design and Counterfactual Inference. CoRR Tanner Fiez, Sergio Gamez, Arick Chen, Houssam Nassif, Lalit Jain
2022	A*	`conf`	An Experimental Design Approach for Regret Minimization in Logistic Bandits. AAAI Blake Mason, Kwang-Sung Jun, Lalit Jain
2022	J	`jnl`	An Experimental Design Approach for Regret Minimization in Logistic Bandits. CoRR Blake Mason, Kwang-Sung Jun, Lalit Jain
2022	A*	`conf`	Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin Jamieson, Lalit Jain
2022	J	`jnl`	Instance-optimal PAC Algorithms for Contextual Bandits. CoRR Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin Jamieson, Lalit Jain
2022	J	`jnl`	Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Comput. Biol. Jennifer Brennan, Lalit Jain, Sofia Garman, Ann E. Donnelly, Erik Scott Wright, Kevin Jamieson
2021	A*	`conf`	Improved Algorithms for Agnostic Pool-based Active Classification. ICML Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson
2021	J	`jnl`	Improved Algorithms for Agnostic Pool-based Active Classification. CoRR Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson
2021	A*	`conf`	Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason
2021	A*	`conf`	Selective Sampling for Online Best-arm Identification. NeurIPS Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson
2021	J	`jnl`	Selective Sampling for Online Best-arm Identification. CoRR Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson
2020	J	`jnl`	A New Perspective on Pool-Based Active Classification and False-Discovery Control. CoRR Lalit Jain, Kevin Jamieson
2020	A*	`conf`	An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. NeurIPS Julian Katz-Samuels, Lalit Jain, Zohar S. Karnin, Kevin Jamieson
2020	J	`jnl`	An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. CoRR Julian Katz-Samuels, Lalit Jain, Zohar S. Karnin, Kevin Jamieson
2020	J	`jnl`	Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR Kwang-Sung Jun, Lalit Jain, Houssam Nassif
2020	A	`conf`	Spectral Methods for Ranking with Scarce Data. UAI Lalit Jain, Anna C. Gilbert, Umang Varma
2020	J	`jnl`	Spectral Methods for Ranking with Scarce Data. CoRR Umang Varma, Lalit Jain, Anna C. Gilbert
2019	A*	`conf`	A New Perspective on Pool-Based Active Classification and False-Discovery Control. NeurIPS Lalit Jain, Kevin Jamieson
2019	A*	`conf`	Sequential Experimental Design for Transductive Linear Bandits. NeurIPS Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian J. Ratliff
2019	J	`jnl`	Sequential Experimental Design for Transductive Linear Bandits. CoRR Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian J. Ratliff
2018	J	`jnl`	A Bandit Approach to Multiple Testing with False Discovery Control. CoRR Kevin Jamieson, Lalit Jain
2018	A*	`conf`	A Bandit Approach to Sequential Experimental Design with False Discovery Control. NeurIPS Kevin Jamieson, Lalit Jain
2018	C	`conf`	Deep Learning for Military Image Captioning. FUSION Subrata Das, Lalit Jain, Arup Das
2018	A*	`conf`	Firing Bandits: Optimizing Crowdfunding. ICML Lalit Jain, Kevin Jamieson
2018	Misc	`conf`	The Landscape of Non-Convex Quadratic Feasibility. ICASSP Amanda Bower, Lalit Jain, Laura Balzano
2017	—	`conf`	If it ain't broke, don't fix it: Sparse metric repair. Allerton Anna C. Gilbert, Lalit Jain
2017	J	`jnl`	If it ain't broke, don't fix it: Sparse metric repair. CoRR Anna C. Gilbert, Lalit Jain