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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)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 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)Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming, , and . Proceedings of the 17th International Conference on Educational Data Mining, page 341--348. Atlanta, Georgia, USA, International Educational Data Mining Society, (July 2024)On the Asymptotic Optimality of Maximum Margin Bayesian Networks., and . AISTATS, volume 31 of JMLR Workshop and Conference Proceedings, page 590-598. JMLR.org, (2013)On Theoretical Properties of Sum-Product Networks., , , and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)The Most Generative Maximum Margin Bayesian Networks., , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 235-243. JMLR.org, (2013)