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Actively Learning Hemimetrics with Applications to Eliciting User Preferences.

, , and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 412-420. JMLR.org, (2016)

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Coordinated Online Learning With Applications to Learning User Preferences., , , and . CoRR, (2017)Evaluating Rule-based Programming and ReinforcementLearning for Personalising an Intelligent System., , , and . IUI Workshops, volume 2327 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes., , , and . AIED (1), volume 13355 of Lecture Notes in Computer Science, page 28-40. Springer, (2022)Learning User Preferences to Incentivize Exploration in the Sharing Economy., , , and . AAAI, page 2248-2256. AAAI Press, (2018)Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model., , , , , and . CoRR, (2019)HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals., , , , , and . AABI, volume 118 of Proceedings of Machine Learning Research, page 1-8. PMLR, (2019)Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model., , , , , and . NeurIPS, page 14791-14802. (2019)Information Directed Reward Learning for Reinforcement Learning., , , , and . NeurIPS, page 3850-3862. (2021)Sum-Product Networks for Sequence Labeling., , and . CoRR, (2018)Successor Uncertainties: exploration and uncertainty in temporal difference learning., , , , and . CoRR, (2018)