 A Competitive Algorithm for Agnostic Active LearningNeural Information Processing Systems (NeurIPS), 2023 |
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 Teaching an Active Learner with Contrastive ExamplesNeural Information Processing Systems (NeurIPS), 2021 |
 Problem Dependent View on Structured Thresholding Bandit ProblemsInternational Conference on Machine Learning (ICML), 2021 |
 Adaptive Region-Based Active LearningInternational Conference on Machine Learning (ICML), 2020 |
 A Tight Analysis of Greedy Yields Subexponential Time Approximation for
Uniform Decision TreeACM-SIAM Symposium on Discrete Algorithms (SODA), 2019 |
 Flattening a Hierarchical Clustering through Active LearningNeural Information Processing Systems (NeurIPS), 2019 |
 Diameter-based Interactive Structure DiscoveryInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 |
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 Generalized Binary Search For Split-Neighborly ProblemsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2018 |
 Convergence Rates of Active Learning for Maximum Likelihood EstimationNeural Information Processing Systems (NeurIPS), 2015 |
 Beyond Disagreement-based Agnostic Active LearningNeural Information Processing Systems (NeurIPS), 2014 |
 Near-Optimally Teaching the Crowd to ClassifyInternational Conference on Machine Learning (ICML), 2014 |
 Noisy Bayesian Active LearningAllerton Conference on Communication, Control, and Computing (Allerton), 2013 |
 Recommending with an Agenda: Active Learning of Private Attributes using
Matrix FactorizationACM Conference on Recommender Systems (RecSys), 2013 |
 Active Sensing as Bayes-Optimal Sequential Decision MakingConference on Uncertainty in Artificial Intelligence (UAI), 2013 |
 Sequentiality and Adaptivity Gains in Active Hypothesis TestingIEEE Journal on Selected Topics in Signal Processing (JSTSP), 2012 |
 Active and passive learning of linear separators under log-concave
distributionsAnnual Conference Computational Learning Theory (COLT), 2012 |
 Comparison-Based Learning with Rank NetsInternational Conference on Machine Learning (ICML), 2012 |
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