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ORCIDLalit Jain
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2020 – today
- 2024
[c27]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: 1585-1593- [c26]Zhaoqi Li, Kevin G. Jamieson, Lalit Jain:
Optimal Exploration is no harder than Thompson Sampling. AISTATS 2024: 1684-1692 - [c25]Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu:
Pessimistic Off-Policy Multi-Objective Optimization. AISTATS 2024: 2980-2988 - [c24]Tanner Fiez
, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain:
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. WWW 2024: 3586-3597 - [i27]Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain:
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. CoRR abs/2402.10870 (2024) - [i26]Aniruddha Bhargava, Lalit Jain, Branislav Kveton, Ge Liu, Subhojyoti Mukherjee:
Off-Policy Evaluation from Logged Human Feedback. CoRR abs/2406.10030 (2024) - [i25]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 abs/2406.10522 (2024) - [i24]Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain:
Adaptive Experimentation When You Can't Experiment. CoRR abs/2406.10738 (2024) - 2023
- [c23]Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber, Blake Mason, Houssam Nassif, Lalit Jain:
Experimental Designs for Heteroskedastic Variance. NeurIPS 2023 - [i23]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 abs/2307.15154 (2023) - [i22]Zhaoqi Li, Kevin G. Jamieson, Lalit Jain:
Optimal Exploration is no harder than Thompson Sampling. CoRR abs/2310.06069 (2023) - [i21]Artin Tajdini, Lalit Jain, Kevin Jamieson:
Minimax Optimal Submodular Optimization with Bandit Feedback. CoRR abs/2310.18465 (2023) - [i20]Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu:
Pessimistic Off-Policy Multi-Objective Optimization. CoRR abs/2310.18617 (2023) - [i19]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. CoRR abs/2312.08559 (2023) - [i18]Shyam Nuggehalli, Jifan Zhang, Lalit Jain, Robert D. Nowak:
DIRECT: Deep Active Learning under Imbalance and Label Noise. CoRR abs/2312.09196 (2023) - 2022
- [j1]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) - [c22]Blake Mason, Kwang-Sung Jun, Lalit Jain:
An Experimental Design Approach for Regret Minimization in Logistic Bandits. AAAI 2022: 7736-7743 - [c21]Blake Mason, Lalit Jain, Subhojyoti Mukherjee, Romain Camilleri, Kevin G. Jamieson, Robert D. Nowak:
Nearly Optimal Algorithms for Level Set Estimation. AISTATS 2022: 7625-7658 - [c20]Romain Camilleri, Andrew Wagenmaker, Jamie H. Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. NeurIPS 2022 - [c19]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS 2022 - [i17]Blake Mason, Kwang-Sung Jun, Lalit Jain:
An Experimental Design Approach for Regret Minimization in Logistic Bandits. CoRR abs/2202.02407 (2022) - [i16]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. CoRR abs/2206.11183 (2022) - [i15]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR abs/2207.02357 (2022) - [i14]Tanner Fiez, Sergio Gamez, Arick Chen, Houssam Nassif, Lalit Jain:
Adaptive Experimental Design and Counterfactual Inference. CoRR abs/2210.14369 (2022) - 2021
- [c18]Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML 2021: 5148-5157 - [c17]Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson:
Improved Algorithms for Agnostic Pool-based Active Classification. ICML 2021: 5334-5344 - [c16]Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson:
Selective Sampling for Online Best-arm Identification. NeurIPS 2021: 11071-11082 - [i13]Julian Katz-Samuels, Jifan Zhang, Lalit Jain, Kevin Jamieson:
Improved Algorithms for Agnostic Pool-based Active Classification. CoRR abs/2105.06499 (2021) - [i12]Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson:
Selective Sampling for Online Best-arm Identification. CoRR abs/2110.14864 (2021) - [i11]Blake Mason, Romain Camilleri, Subhojyoti Mukherjee, Kevin Jamieson, Robert D. Nowak, Lalit Jain:
Nearly Optimal Algorithms for Level Set Estimation. CoRR abs/2111.01768 (2021) - 2020
- [c15]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 - [c14]Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak:
Finding All $\epsilon$-Good Arms in Stochastic Bandits. NeurIPS 2020 - [c13]Lalit Jain, Anna C. Gilbert, Umang Varma:
Spectral Methods for Ranking with Scarce Data. UAI 2020: 609-618 - [i10]Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert D. Nowak:
Finding All ε-Good Arms in Stochastic Bandits. CoRR abs/2006.08850 (2020) - [i9]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 abs/2006.11685 (2020) - [i8]Umang Varma, Lalit Jain, Anna C. Gilbert:
Spectral Methods for Ranking with Scarce Data. CoRR abs/2007.01346 (2020) - [i7]Lalit Jain, Kevin G. Jamieson:
A New Perspective on Pool-Based Active Classification and False-Discovery Control. CoRR abs/2008.06555 (2020) - [i6]Kwang-Sung Jun, Lalit Jain, Houssam Nassif:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR abs/2011.11222 (2020)
2010 – 2019
- 2019
- [c12]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. NeurIPS 2019: 10666-10676 - [c11]Lalit Jain, Kevin G. Jamieson:
A New Perspective on Pool-Based Active Classification and False-Discovery Control. NeurIPS 2019: 13992-14003 - [i5]Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian J. Ratliff:
Sequential Experimental Design for Transductive Linear Bandits. CoRR abs/1906.08399 (2019) - 2018
- [c10]Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak:
Adaptive Sampling for Coarse Ranking. AISTATS 2018: 1839-1848 - [c9]Subrata Das, Lalit Jain, Arup Das:
Deep Learning for Military Image Captioning. FUSION 2018: 2165-2171 - [c8]Amanda Bower, Lalit Jain, Laura Balzano:
The Landscape of Non-Convex Quadratic Feasibility. ICASSP 2018: 3974-3978 - [c7]Lalit Jain, Kevin G. Jamieson:
Firing Bandits: Optimizing Crowdfunding. ICML 2018: 2211-2219 - [c6]Kevin G. Jamieson, Lalit Jain:
A Bandit Approach to Sequential Experimental Design with False Discovery Control. NeurIPS 2018: 3664-3674 - [i4]Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak:
Adaptive Sampling for Coarse Ranking. CoRR abs/1802.07176 (2018) - [i3]Kevin G. Jamieson, Lalit Jain:
A Bandit Approach to Multiple Testing with False Discovery Control. CoRR abs/1809.02235 (2018) - 2017
- [c5]Anna C. Gilbert, Lalit Jain:
If it ain't broke, don't fix it: Sparse metric repair. Allerton 2017: 612-619 - [c4]Blake Mason, Lalit Jain, Robert D. Nowak:
Learning Low-Dimensional Metrics. NIPS 2017: 4139-4147 - [c3]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: 113-119 - [i2]Anna C. Gilbert, Lalit Jain:
If it ain't broke, don't fix it: Sparse metric repair. CoRR abs/1710.10655 (2017) - 2016
- [c2]Lalit Jain, Kevin G. Jamieson, Robert D. Nowak:
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. NIPS 2016: 2703-2711 - [i1]Lalit Jain, Kevin G. Jamieson, Robert D. Nowak:
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding. CoRR abs/1606.07081 (2016) - 2015
- [c1]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: 2656-2664
Coauthor Index
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last updated on 2024-07-19 18:22 CEST by the dblp team
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