Preprints
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DIRECT: Deep Active Learning under Imbalance and Label Noise, S. Nuggehalli, J. Zhang, L. Jain, R. Nowak
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Fair Active Learning in Low-Data Regimes, R. Camilleri, A. Wagenmaker, J. Morgenstern, L. Jain, K. Jamieson
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Minimax Optimal Submodular Optimization with Bandit Feedback, A. Tajdini, L. Jain, K. Jamieson
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Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models. L. Jain, Z. Li, E. Loghmani, B. Mason, H. Yoganarasimhan
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Learning to Actively Learn: A Robust Approach. J. Zhang, L. Jain, K. Jamieson
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Convergence rates for ordinal embedding. J. Ellenberg, L. Jain
Publications
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Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. T. Fiez, H. Nassif, A. Chen, S. Gamez, L. Jain, WWW 2024.
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Optimal Exploration is no harder than Thompson Sampling. Z. Li, K. Jamieson, L. Jain, AISTATS 2024.
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Pessimistic Off-Policy Multi-Objective Optimization. S. Alizadeh, A. Bhargava, K. Gopalswamy, L. Jain, B. Kveton, G. Liu, AISTATS 2024.
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A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. Z. Xiong, R. Camilleri, M. Fazel, L. Jain, K. Jamieson, AISTATS 2024.
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Experimental Designs for Heteroskedastic Variance. J. Weltz, T. Fiez, A. Volfovsky, E. Laber, B. Mason, H. Nassif, K. Jamieson, L. Jain. Neurips 2023.
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Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. J Brennan, L Jain, S Garman, AE Donnelly, ES Wright, K Jamieson PLoS computational biology 18(7) 2022
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Instance-optimal PAC Algorithms for Contextual Bandits. Z. Li, L. Ratliff, H. Nassif, K. Jamieson, L. Jain. Neurips 2022.
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Active Learning with Safety Constraints. R. Camilleri, A. Wagenmaker, J. Morgenstern, L. Jain, K. Jamieson. Neurips 2022.
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Nearly Optimal Algorithms for Level Set Estimation. B. Mason, R. Camilleri, S. Mukherjee, K. Jamieson, R. Nowak, L. Jain. AISTATS 2022.
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An Experimental Design Approach for Regret Minimization in Logistic Bandits. B. Mason, K. Jun, L. Jain. AAAI 2022.
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Selective Sampling for Online Best-arm Identification. Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G Jamieson. Neurips 2021.
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Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. K. Jun, L. Jain, B. Mason, H. Nassif. ICML 2021.
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Improved Algorithms for Agnostic Pool-based Active Classification. J Katz-Samuels, J Zhang, L Jain, K Jamieson. ICML 2021.
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Finding All ε-Good Arms in Stochastic Bandits. B. Mason, L. Jain, A. Tripathy, R. Nowak. Neurips 2020.
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An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. J. Katz-Samuels, L. Jain, Z. Karnin, K. Jamieson. Neurips 2020.
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Spectral Methods for Ranking with Scarce Data. U. Varma, L. Jain, A.C. Gilbert. Uncertainty in Artificial Intelligences, 2020.
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A New Perspective on Pool-Based Active Classification and False-Discovery Control. L. Jain, K. Jamieson. Neurips 2019.
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Sequential Experimental Design for Transductive Linear Bandits. T Fiez, L Jain, K Jamieson, L Ratliff. Neurips 2019.
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Firing Bandits: Optimizing Crowdfunding. L. Jain, K. Jamieson. ICML 2018.
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A Bandit Approach to Multiple Testing with False Discovery Control. K. Jamieson, L. Jain. NIPS 2018.
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Adaptive Sampling for Coarse Ranking, S. Katariya, L. Jain, N. Sengupta, J. Evans, R. Nowak. AISTATS, 2018.
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The Landscape of Non-Convex Quadratic Feasibility. A. Bower, L. Jain, L. Balzano, ICASSP 2018.
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Learning Low-Dimensional Metrics, L. Jain, B. Mason, R. Nowak, NIPS 2017.
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If it ain't broke, don't fix it: Sparse metric repair. A. Gilbert, L. Jain, Allerton 2017
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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding,L. Jain, K. Jamieson, R. Nowak, NIPS 2016.
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NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning, K. Jamieson, L. Jain, C. Fernandez, N. Glattard, R. Nowak, NIPS, 2015.