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\n  \n 2022\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Human-Centered Reinforcement Learning for Personalized Self-Management Strategies.\n \n \n \n\n\n \n Urteaga, I.; and Elhadad, N.\n\n\n \n\n\n\n May 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Conference{c-Urteaga2022d,\n  author    = {I{\\~n}igo Urteaga and No\\'{e}mie Elhadad},\n  booktitle = {CHI 2022 Workshop ``Grand Challenges for Personal Informatics and AI''},\n  title     = {{Human-Centered Reinforcement Learning for Personalized Self-Management Strategies}},\n  year      = {2022},\n  month     = may,\n}\n\n
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\n \n\n \n \n \n \n \n Sequential Monte Carlo for Multi-Armed Bandit Agents.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n April 2022.\n \n\n\n\n
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@Conference{c-Urteaga2022c,\n  author    = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  booktitle = {5th Workshop on Sequential Monte Carlo Methods},\n  title     = {{Sequential Monte Carlo for Multi-Armed Bandit Agents}},\n  year      = {2022},\n  month     = apr,\n}\n\n
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\n \n\n \n \n \n \n \n Gaussian Process Thompson sampling for Bayesian optimization of dynamic masking-based language model pre-training.\n \n \n \n\n\n \n Urteaga, I.; Draïdia, M.; Lancewicki, T.; and Khadivi, S.\n\n\n \n\n\n\n December 2022.\n \n\n\n\n
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@Conference{c-Urteaga2022a,\n  author    = {I{\\~n}igo Urteaga and Moulay-Za\\"idane Dra\\"idia and Tomer Lancewicki and Shahram Khadivi},\n  booktitle = {NeurIPS 2022 Workshop ``Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems''},\n  title     = {{Gaussian Process Thompson sampling for Bayesian optimization of dynamic masking-based language model pre-training}},\n  year      = {2022},\n  month     = dec,\n}\n\n
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\n \n\n \n \n \n \n \n Thompson sampling for interactive Bayesian optimization of dynamic masking-based language model pre-training.\n \n \n \n\n\n \n Urteaga, I.; Draïdia, M.; Lancewicki, T.; and Khadivi, S.\n\n\n \n\n\n\n December 2022.\n \\textttLightning Talk\n\n\n\n
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@Conference{c-Urteaga2022b,\n  author    = {I{\\~n}igo Urteaga and Moulay-Za\\"idane Dra\\"idia and Tomer Lancewicki and Shahram Khadivi},\n  booktitle = {EMNLP 2022 Workshop ``Novel Ideas in Learning-to-Learn through Interaction'' (NILLI)},\n  title     = {{Thompson sampling for interactive Bayesian optimization of dynamic masking-based language model pre-training}},\n  year      = {2022},\n  month     = dec,\n  note      = {\\texttt{\\textit{Lightning Talk}}},\n}\n\n
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\n  \n 2020\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data.\n \n \n \n\n\n \n Li, K.; Urteaga, I.; Shea, A.; Vitzthum, V.; Wiggins, C. H; and Elhadad, N.\n\n\n \n\n\n\n 2020.\n \\textttSpotlight talk, Health Sciences track\n\n\n\n
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@Conference{c-Li2020b,\n  author    = {Kathy Li and I{\\~n}igo Urteaga and Amanda Shea and Virginia Vitzthum and Chris H Wiggins and No\\'{e}mie Elhadad},\n  title     = {{A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data}},\n  booktitle = {Machine Learning in Science \\& Engineering (MLSE2020)},\n  year      = {2020},\n  note      = {\\texttt{\\textit{Spotlight talk}}, Health Sciences track},\n}\n\n
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\n \n\n \n \n \n \n \n \n A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data.\n \n \n \n \n\n\n \n Li, K.; Urteaga, I.; Shea, A.; Vitzthum, V.; Wiggins, C. H; and Elhadad, N.\n\n\n \n\n\n\n 2020.\n \\textttContributed Talk\n\n\n\n
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@Conference{c-Li2020a,\n  author    = {Kathy Li and I{\\~n}igo Urteaga and Amanda Shea and Virginia Vitzthum and Chris H Wiggins and No\\'{e}mie Elhadad},\n  booktitle = {NeurIPS 2020 Workshop ``Machine Learning for Mobile Health''},\n  title     = {{A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data}},\n  year      = {2020},\n  note      = {\\texttt{\\textit{Contributed Talk}}},\n  url       = {https://drive.google.com/open?id=1DKf8RBEtjzWOf8JMRdAcaT6iO3ZS5FSt},\n}\n\n
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\n \n\n \n \n \n \n \n Sequential Monte Carlo for Dynamic Softmax Bandits.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n 2018.\n \n\n\n\n
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@Conference{c-Urteaga2018a,\n  author    = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  title     = {{Sequential Monte Carlo for Dynamic Softmax Bandits}},\n  booktitle = {1st Symposium on Advances in Approximate Bayesian Inference (AABI 2018)},\n  year      = {2018},\n}\n\n
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\n \n\n \n \n \n \n \n Nonparametric Gaussian mixture models for the multi-armed contextual bandit.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n 2018.\n \n\n\n\n
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@Conference{c-Urteaga2018b,\n  author    = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  title     = {{Nonparametric Gaussian mixture models for the multi-armed contextual bandit}},\n  booktitle = {NeurIPS 2018 Workshop ``All of Bayesian Nonparametrics (Especially the Useful Bits)''},\n  year      = {2018},\n}\n\n
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\n \n\n \n \n \n \n \n Bandits with sequentially observed rewards: a Bayesian generative Thompson sampling approach.\n \n \n \n\n\n \n Urteaga, I.; and Wiggins, C. H.\n\n\n \n\n\n\n 2018.\n \n\n\n\n
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@Conference{c-Urteaga2018c,\n  author    = {I{\\~n}igo Urteaga and Chris H. Wiggins},\n  title     = {{Bandits with sequentially observed rewards: a Bayesian generative Thompson sampling approach}},\n  booktitle = {NeurIPS 2018 Workshop ``Reinforcement Learning under Partial Observability''},\n  year      = {2018},\n}\n\n
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\n  \n 2017\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes.\n \n \n \n \n\n\n \n Urteaga, I.; Albers, D. J.; Wheeler, M. V.; Druet, A.; Raffauf, H.; and Elhadad, N.\n\n\n \n\n\n\n 2017.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Conference{c-Urteaga2017b,\n  author        = {I{\\~n}igo Urteaga and David J. Albers and Marija Vlajic Wheeler and Anna Druet and Hans Raffauf and No\\'{e}mie Elhadad},\n  title         = {{Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes}},\n  booktitle     = {NeurIPS 2017 Workshop ``Machine Learning for Health''},\n  year          = {2017},\n  url           = {https://arxiv.org/abs/1712.00117},\n}\n\n
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