NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning. - Kevin G. Jamieson; Lalit Jain; Chris Fernandez; Nicholas J. Glattard; Robert D. Nowak (NIPS, 2015)

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. - Lalit Jain; Kevin G. Jamieson; Robert D. Nowak (NIPS, 2016)

If it ain't broke, don't fix it: Sparse metric repair. - Anna C. Gilbert; Lalit Jain (Allerton, 2017)

Learning Low-Dimensional Metrics. - Blake Mason; Lalit Jain; Robert D. Nowak (NIPS, 2017)

NEXT: A system to easily connect crowdsourcing and adaptive data collection. - Scott Sievert; Daniel Ross; Lalit Jain; Kevin Jamieson 001; Robert Nowak 001; Robert Mankoff (SciPy, 2017)

Adaptive Sampling for Coarse Ranking. - Sumeet Katariya; Lalit Jain; Nandana Sengupta; James Evans; Robert Nowak 001 (AISTATS, 2018)

Deep Learning for Military Image Captioning. - Subrata Das; Lalit Jain; Arup Das (FUSION, 2018)

The Landscape of Non-Convex Quadratic Feasibility. - Amanda Bower; Lalit Jain; Laura Balzano (ICASSP, 2018)

Firing Bandits: Optimizing Crowdfunding. - Lalit Jain; Kevin G. Jamieson (ICML, 2018)

A Bandit Approach to Sequential Experimental Design with False Discovery Control. - Kevin G. Jamieson; Lalit Jain (NeurIPS, 2018)

A Bandit Approach to Multiple Testing with False Discovery Control. - Kevin G. Jamieson; Lalit Jain (CoRR, 2018)

Sequential Experimental Design for Transductive Linear Bandits. - Tanner Fiez; Lalit Jain; Kevin G. Jamieson; Lillian J. Ratliff (NeurIPS, 2019)

A New Perspective on Pool-Based Active Classification and False-Discovery Control. - Lalit Jain; Kevin G. Jamieson (NeurIPS, 2019)

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. - Julian Katz-Samuels; Lalit Jain; Zohar Karnin; Kevin G. Jamieson (NeurIPS, 2020)

Finding All $ epsilon$-Good Arms in Stochastic Bandits. - Blake Mason; Lalit Jain; Ardhendu Tripathy; Robert Nowak 001 (NeurIPS, 2020)

Spectral Methods for Ranking with Scarce Data. - Lalit Jain; Anna C. Gilbert; Umang Varma (UAI, 2020)

Finding All ε-Good Arms in Stochastic Bandits. - Blake Mason; Lalit Jain; Ardhendu Tripathy; Robert D. Nowak (CoRR, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif (CoRR, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif; Blake Mason (ICML, 2021)

Improved Algorithms for Agnostic Pool-based Active Classification. - Julian Katz-Samuels; Jifan Zhang; Lalit Jain; Kevin Jamieson 001 (ICML, 2021)

Selective Sampling for Online Best-arm Identification. - Romain Camilleri; Zhihan Xiong; Maryam Fazel; Lalit Jain; Kevin G. Jamieson (NeurIPS, 2021)

An Experimental Design Approach for Regret Minimization in Logistic Bandits. - Blake Mason; Kwang-Sung Jun; Lalit Jain (AAAI, 2022)

Nearly Optimal Algorithms for Level Set Estimation. - Blake Mason; Lalit Jain; Subhojyoti Mukherjee; Romain Camilleri; Kevin G. Jamieson; Robert D. Nowak (AISTATS, 2022)

Active Learning with Safety Constraints. - Romain Camilleri; Andrew Wagenmaker; Jamie H. Morgenstern; Lalit Jain; Kevin G. Jamieson (NeurIPS, 2022)

Instance-optimal PAC Algorithms for Contextual Bandits. - Zhaoqi Li; Lillian J. Ratliff; Houssam Nassif; Kevin G. Jamieson; Lalit Jain (NeurIPS, 2022)

Adaptive Experimental Design and Counterfactual Inference. - Tanner Fiez; Sergio Gamez; Arick Chen; Houssam Nassif; Lalit Jain (CoRR, 2022)

Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. - Jennifer Brennan; Lalit Jain; Sofia Garman; Ann E. Donnelly; Erik Scott Wright; Kevin G. Jamieson (PLoS Comput. Biol., 2022)

Experimental Designs for Heteroskedastic Variance. - Justin Weltz; Tanner Fiez; Alexander Volfovsky; Eric Laber; Blake Mason; Houssam Nassif; Lalit Jain (NeurIPS, 2023)

Minimax Optimal Submodular Optimization with Bandit Feedback. - Artin Tajdini; Lalit Jain; Kevin Jamieson 001 (CoRR, 2023)

DIRECT: Deep Active Learning under Imbalance and Label Noise. - Shyam Nuggehalli; Jifan Zhang; Lalit Jain; Robert D. Nowak (CoRR, 2023)

Pessimistic Off-Policy Multi-Objective Optimization. - Shima Alizadeh; Aniruddha Bhargava; Karthick Gopalswamy; Lalit Jain; Branislav Kveton; Ge Liu (AISTATS, 2024)

Optimal Exploration is no harder than Thompson Sampling. - Zhaoqi Li; Kevin G. Jamieson; Lalit Jain (AISTATS, 2024)

A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. - Zhihan Xiong; Romain Camilleri; Maryam Fazel; Lalit Jain; Kevin G. Jamieson (AISTATS, 2024)

Nearly Minimax Optimal Submodular Maximization with Bandit Feedback. - Artin Tajdini; Lalit Jain; Kevin G. Jamieson (NeurIPS, 2024)

Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. - Jifan Zhang; Lalit Jain; Yang Guo; Jiayi Chen; Kuan Lok Zhou; Siddharth Suresh; Andrew Wagenmaker; Scott Sievert; Timothy T. Rogers; Kevin G. Jamieson; Bob Mankoff; Robert Nowak 001 (NeurIPS, 2024)

Adaptive Experimentation When You Can't Experiment. - Yao Zhao; Kwang-Sung Jun; Tanner Fiez; Lalit Jain (NeurIPS, 2024)

Fair Active Learning in Low-Data Regimes. - Romain Camilleri; Andrew Wagenmaker; Jamie Morgenstern; Lalit Jain; Kevin Jamieson 001 (UAI, 2024)

Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. - Tanner Fiez; Houssam Nassif; Yu-Cheng Chen; Sergio Gamez; Lalit Jain (WWW, 2024)

Off-Policy Evaluation from Logged Human Feedback. - Aniruddha Bhargava; Lalit Jain; Branislav Kveton; Ge Liu; Subhojyoti Mukherjee (CoRR, 2024)

Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models. - Lalit Jain; Zhaoqi Li; Erfan Loghmani; Blake Mason; Hema Yoganarasimhan (Mark. Sci., 2024)

An Empirical Risk Minimization Approach for Offline Inverse Reinforcement Learning and Dynamic Discrete Choice Models. - Enoch Hyunwook Kang; Hema Yoganarasimhan; Lalit Jain (EC, 2025)

Gradients can train reward models: An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model. - Enoch H. Kang; Hema Yoganarasimhan; Lalit Jain (CoRR, 2025)

Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs. - Kuan Lok Zhou; Jiayi Chen; Siddharth Suresh; Reuben Narad; Timothy T. Rogers; Lalit Jain; Robert D. Nowak; Bob Mankoff; Jifan Zhang (CoRR, 2025)

Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench. - Reuben Narad; Siddharth Suresh; Jiayi Chen; Pine S. L. Dysart-Bricken; Bob Mankoff; Rob Nowak 001; Jifan Zhang; Lalit Jain (CoRR, 2025)