Lalit Jain

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 55

Top Topics

Selective Sampling

Confidence Bounds

Experimental Designs

Active Learning

Top Venues

Publications

Tanner Fiez, Houssam Nassif, Arick Chen, Sergio Gamez, Lalit Jain
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. CoRR (2024)
Zhaoqi Li, Kevin G. Jamieson, Lalit Jain
Optimal Exploration is no harder than Thompson Sampling. AISTATS (2024)
Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee
Off-Policy Evaluation from Logged Human Feedback. CoRR (2024)
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
Pessimistic Off-Policy Multi-Objective Optimization. AISTATS (2024)
Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
Adaptive Experimentation When You Can't Experiment. CoRR (2024)
Jifan Zhang, Lalit Jain, Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh, Andrew Wagenmaker, Scott Sievert, Timothy T. Rogers, Kevin Jamieson, Robert Mankoff, Robert Nowak
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. CoRR (2024)
Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. WWW (2024)
Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. AISTATS (2024)
Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. CoRR (2023)
Zhaoqi Li, Kevin G. Jamieson, Lalit Jain
Optimal Exploration is no harder than Thompson Sampling. CoRR (2023)
Shyam Nuggehalli, Jifan Zhang, Lalit Jain, Robert D. Nowak
DIRECT: Deep Active Learning under Imbalance and Label Noise. CoRR (2023)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
Fair Active Learning in Low-Data Regimes. CoRR (2023)
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
Pessimistic Off-Policy Multi-Objective Optimization. CoRR (2023)
Artin Tajdini, Lalit Jain, Kevin Jamieson
Minimax Optimal Submodular Optimization with Bandit Feedback. CoRR (2023)
Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber, Blake Mason, Houssam Nassif, Lalit Jain
Experimental Designs for Heteroskedastic Variance. NeurIPS (2023)
Jennifer Brennan, Lalit Jain, Sofia Garman, Ann E. Donnelly, Erik Scott Wright, Kevin G. Jamieson
Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Comput. Biol. 18 (7) (2022)
Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin G. Jamieson, Robert D. Nowak
Nearly Optimal Algorithms for Level Set Estimation. AISTATS (2022)
Tanner Fiez, Sergio Gamez, Arick Chen, Houssam Nassif, Lalit Jain
Adaptive Experimental Design and Counterfactual Inference. CoRR (2022)
Blake Mason, Kwang-Sung Jun, Lalit Jain
An Experimental Design Approach for Regret Minimization in Logistic Bandits. AAAI (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. CoRR (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. NeurIPS (2022)
Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR (2022)
Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS (2022)
Blake Mason, Kwang-Sung Jun, Lalit Jain
An Experimental Design Approach for Regret Minimization in Logistic Bandits. CoRR (2022)
Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson
Improved Algorithms for Agnostic Pool-based Active Classification. ICML (2021)
Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML (2021)
Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
Selective Sampling for Online Best-arm Identification. NeurIPS (2021)
Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson
Improved Algorithms for Agnostic Pool-based Active Classification. CoRR (2021)
Blake Mason, Romain Camilleri, Subhojyoti Mukherjee, Kevin Jamieson, Robert D. Nowak, Lalit Jain
Nearly Optimal Algorithms for Level Set Estimation. CoRR (2021)
Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson
Selective Sampling for Online Best-arm Identification. CoRR (2021)
Lalit Jain, Kevin G. Jamieson
A New Perspective on Pool-Based Active Classification and False-Discovery Control. CoRR (2020)
Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak
Finding All $\epsilon$-Good Arms in Stochastic Bandits. NeurIPS (2020)
Lalit Jain, Anna C. Gilbert, Umang Varma
Spectral Methods for Ranking with Scarce Data. UAI (2020)
Kwang-Sung Jun, Lalit Jain, Houssam Nassif
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR (2020)
Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert D. Nowak
Finding All ε-Good Arms in Stochastic Bandits. CoRR (2020)
Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin G. Jamieson
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. CoRR (2020)
Umang Varma, Lalit Jain, Anna C. Gilbert
Spectral Methods for Ranking with Scarce Data. CoRR (2020)
Julian Katz-Samuels, Lalit Jain, Zohar Karnin, Kevin G. Jamieson
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. NeurIPS (2020)
Lalit Jain, Kevin G. Jamieson
A New Perspective on Pool-Based Active Classification and False-Discovery Control. NeurIPS (2019)
Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff
Sequential Experimental Design for Transductive Linear Bandits. CoRR (2019)
Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff
Sequential Experimental Design for Transductive Linear Bandits. NeurIPS (2019)
Kevin G. Jamieson, Lalit Jain
A Bandit Approach to Sequential Experimental Design with False Discovery Control. NeurIPS (2018)
Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak
Adaptive Sampling for Coarse Ranking. AISTATS (2018)
Amanda Bower, Lalit Jain, Laura Balzano
The Landscape of Non-Convex Quadratic Feasibility. ICASSP (2018)
Kevin G. Jamieson, Lalit Jain
A Bandit Approach to Multiple Testing with False Discovery Control. CoRR (2018)
Lalit Jain, Kevin G. Jamieson
Firing Bandits: Optimizing Crowdfunding. ICML (2018)
Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak
Adaptive Sampling for Coarse Ranking. CoRR (2018)
Subrata Das, Lalit Jain, Arup Das
Deep Learning for Military Image Captioning. FUSION (2018)
Anna C. Gilbert, Lalit Jain
If it ain't broke, don't fix it: Sparse metric repair. CoRR (2017)
Blake Mason, Lalit Jain, Robert D. Nowak
Learning Low-Dimensional Metrics. NIPS (2017)
Scott Sievert, Daniel Ross, Lalit Jain, Kevin Jamieson, Robert Nowak, Robert Mankoff
NEXT: A system to easily connect crowdsourcing and adaptive data collection. SciPy (2017)
Anna C. Gilbert, Lalit Jain
If it ain't broke, don't fix it: Sparse metric repair. Allerton (2017)
Lalit Jain, Kevin G. Jamieson, Robert D. Nowak
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. CoRR (2016)
Lalit Jain, Kevin G. Jamieson, Robert D. Nowak
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. NIPS (2016)
Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Robert D. Nowak
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning. NIPS (2015)