Publications

N. Grupen, Natasha Jaques, B. Kim, S. Omidshafiei (2022). Concept-based Understanding of Emergent Multi-Agent Behavior. In Preprint.

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M. Abdulhai, C. Crepy, D. Valter, J. Canny, S. Levine, Natasha Jaques (2022). Moral Foundations of Large Language Models. In Preprint.

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R. E. Wang, J. Mu, D. Arumugam, Natasha Jaques, N. Goodman (2022). In the ZONE: Measuring difficulty and progression in curriculum generation. In Preprint.

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S. Krishnan, Natasha Jaques, S. Omidshafiei, D. Zhang, I. Gur, V. J. Reddi, S. Faust (2022). Multi-Agent Reinforcement Learning for Hardware Architecture Search: A Case Study on Domain-Specific DRAM Memory Controller Design. In Preprint.

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M. Abdulhai, Natasha Jaques, S. Levine (2022). Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience. In Preprint.

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S. Wang, C. Montgomery, J. Orbay, V. Birodkar, A. Faust, I. Gur, Natasha Jaques, A. Waters, J. Baldridge, P. Anderson (2022). Less is More: Generating Grounded Navigation Instructions from Landmarks. In Computer Vision and Pattern Recognition (CVPR).

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P. de Looff, R. Duursma, Noordzij. Noordzi, S. Taylor, Natasha Jaques, F. Scheepers, K. De Schepper, S. Koldijk (2022). Wearables: an R package with accompanying Shiny application for signal analysis of a wearable device targeted at clinicians and researchers. In Frontiers in behavioral neuroscience.

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I. Gur, Natasha Jaques, K. Malta, M. Tiwari, H. Lee, A. Faust (2021). Environment Generation for Zero-Shot Compositional Reinforcement Learning. In Neural Information Processing Systems (NeurIPS).

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A. Filos, C. Lyle, Y. Gal, S. Levine, Natasha Jaques*, G. Farquhar* (2021). PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning. In International Conference on Machine Learning (ICML) Oral (top 3% of submissions).

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Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques (2021). Emergent Social Learning via Multi-agent Reinforcement Learning. In International Conference on Machine Learning (ICML); NeurIPS Cooperative AI Workshop Best Paper.

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A. Fickinger*, Natasha Jaques*, S. Parajuli, M. Chang, N. Rhinehart, G. Berseth, S. Russell, S. Levine (2021). Explore and Control with Adversarial Surprise. In ICML Unsupervised Reinforcement Learning workshop.

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D. Lee, Natasha Jaques, J. Kew, D. Eck, D. Schuurmans, A. Faust (2021). Joint Attention for Multi-Agent Coordination and Social Learning. In ICRA Social Intelligence Workshop Spotlight talk.

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Michael Dennis*, Natasha Jaques*, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine (2020). Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design. In Neural Information Processing Systems (NeurIPS) Oral (top 1% of submissions).

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Natasha Jaques*, J. H. Shen*, A. Ghandeharioun, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, R. Picard (2020). Human-Centric Dialog Training via Offline Reinforcement Learning. In Empirical Methods in Natural Language Processing (EMNLP).

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D. Rolnick, P. L. Donti, L. H. Kaack, K. Kochanski, A. Lacoste, K. Sankaran, A. S. Ross, N. Milojevic-Dupont, Natasha Jaques, A. Waldman-Brown, A. Luccioni, T. Maharaj, E. D. Sherwin, S. K. Mukkavilli, K. P. Kording, C. Gomes, A. Y. Ng, D. Hassabis, J. C. Platt, F. Creutzig, J. Chayes, Y. Bengio (2019). Tackling Climate Change with Machine Learning. In ACM Computing Surveys.

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Natasha Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. A. Ortega, D. J. Strouse, J.Z. Leibo, N. de Freitas (2019). Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. In International Conference on Machine Learning (ICML) Best Paper Honourable Mention (top 0.26% of submissions).

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Natasha Jaques (2019). Social and Affective Machine Learning. In Massachusetts Institute of Technology.

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A. Saleh*, Natasha Jaques*, A. Ghandeharioun, J. H. Shen, R. Picard (2019). Hierarchical Reinforcement Learning for Open-Domain Dialog. In Association for the Advancement of Artificial Intelligence (AAAI) Oral (top 7.8% of submissions).

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N. Jones, Natasha Jaques, P. Pataranutaporn, A. Ghandeharioun, R. Picard (2019). Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation. In Affective Computing and Intelligence Interaction (ACII) workshop on Machine Learning for Mental Health.

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A. Ghandeharioun*, J. H. Shen*, Natasha Jaques*, C. Ferguson, N. Jones, A. Lapedriza, R. Picard (2019). Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems. In Neural Information Processing Systems (NeurIPS).

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K. Johnson, S. Taylor, S. Fedor, Natasha Jaques, W. Chen, R. Picard (2018). Vomit Comet Physiology: Autonomic Changes in Novice Flyers. In IEEE Engineering in Medicine and Biology Society (EMBC).

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Natasha Jaques, J. McCleary, J. Engel, D. Ha, F. Bertsch, D. Eck, R. Picard (2018). Learning via Social Awareness: Improving a Deep Generative Sketching Model with Facial Feedback. In International Conference on Learning Representations (ICLR) workshop.

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Natasha Jaques, S. Gu, D. Bahdanau, J. M. Hernandez-Lobato, R. E. Turner, D. Eck (2017). Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. In International Conference on Machine Learning (ICML).

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Natasha Jaques, O. Rudovic, S. Taylor, A. Sano, R. Picard (2017). Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation. In Proceedings of Machine Learning Research.

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Natasha Jaques*, S. Taylor*, E. Nosakhare, A. Sano, R. Picard (2017). Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health. In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper.

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Natasha Jaques, S. Taylor, A. Sano, R. Picard (2017). Multimodal Autoencoder: A Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction. In International Conference on Affective Computing and Intelligent Interaction (ACII).

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S. Taylor, Natasha Jaques, Sano, A. E. Nosakhare, E. B. Klerman, R. Picard (2017). Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students. In Journal of Sleep and Sleep Disorders Research (suppl_1).

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Natasha Jaques, D. McDuff, Y. K. Kim, \& Picard R. Picard R (2016). Understanding and Predicting Bonding in Conversations Using Thin Slices of Facial Expressions and Body Language. In Intelligent Virtual Agents (IVA).

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Natasha Jaques, S. Gu, R. E. Turner, D. Eck (2016). Tuning Recurrent Neural Networks with Reinforcement Learning. In International Conference on Learning Representations (ICLR) - workshop.

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Natasha Jaques, Y. K. Kim, \& Picard R. Picard R (2016). Personality, Attitudes, and Bonding in Conversations. In Intelligent Virtual Agents (IVA).

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S. Taylor, Natasha Jaques, A. Sano, A. Azaria, A. Ghandeharioun, R. Picard (2016). Machine Learning of Sleep and Wake Behaviors to Classify Self-Reported Evening Mood. In Sleep.

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A. Roberts, J. Engel, C. Hawthorne, I. Simon, E. Waite, S. Oore, Natasha Jaques, C. Resnick, D. Eck (2016). Interactive Musical Improvisation with Magenta. In Neural Information Processing Systems (NeurIPS) Best Demo.

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Natasha Jaques, T. Rich, K. Dinakar, N. Farve, W.V. Chen, P. Maes, R. Picard (2016). BITxBIT: Encouraging Behavior Change with N=2 Experiments. In Proceedings of the CHI Conference Extended Abstracts on Human Factors.

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W. Chen, Natasha Jaques, S. Taylor, A. Sano, S. Fedor, \& Picard R. Picard R (2015). Wavelet-based motion artifact removal for Electrodermal Activity. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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Natasha Jaques, W. V. Chen, R. Picard (2015). SmileTracker: Automatically and Unobtrusively Recording Smiles and their Context.. In Proceedings of the CHI Conference Extended Abstracts.

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A. Sano, A. J. Phillips, A. Z. Yu, A. W. McHill, S. Taylor, Natasha Jaques, C. A. Czeisler, E. B. Klerman, R. Picard (2015). Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In Wearable and Implantable Body Sensor Networks (BSN).

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A. Sano, A. Z. Yu, A. W. McHill, A.J. Phillips, S. Taylor, Natasha Jaques, C. A. Czeisler, E. B. Klerman, R. Picard (2015). Prediction of happy-sad mood from daily behaviors and previous sleep history. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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Natasha Jaques*, S. Taylor*, A. Azaria, A. Ghandeharioun, A. Sano, R. Picard (2015). Predicting students' happiness from physiology, phone, mobility, and behavioral data. In International Conference on Affective Computing and Intelligent Interaction (ACII).

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Natasha Jaques*, S. Taylor*, A. Sano, R. Picard (2015). Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing. In Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning.

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Natasha Jaques, N. Farve (2015). Engaging the workplace with challenges. In International Conference on Persuasive Technologies.

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S. Taylor*, Natasha Jaques*, W. Chen, S. Fedor, A. Sano, R. Picard (2015). Automatic identification of artifacts in Electrodermal Activity data. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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V. Xia, Natasha Jaques, S. Taylor, S. Fedor, R. Picard (2015). Active learning for Electrodermal Activity classification. In IEEE Conference on Signal Processing in Medicine and Biology (SPMB).

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Natasha Jaques (2014). Predicting Affect in an Intelligent Tutoring System. In University of British Columbia.

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Natasha Jaques, C. Conati, J. M. Harley, R. Azevedo (2014). Predicting Affect from Gaze Data During Interaction with an Intelligent Tutoring System. In Intelligent Tutoring Systems.

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C. Conati, Natasha Jaques, M. Muir (2013). Understanding attention to adaptive hints in educational games: an eye-tracking study. In International Journal of Artificial Intelligence in Education.

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Natasha Jaques (2013). Fast Johnson–Lindenstrauss transform for classification of high dimensional data. In Unpublished manuscript.

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Natasha Jaques (2013). Emotionally Adaptive Intelligent Tutoring Systems using POMDPs. In Unpublished manuscript.

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Natasha Jaques, J. Nutini (2013). A Comparison of Random Forests and Dropout Nets for Sign Language Recognition with the Kinect. In Unpublished manuscript.

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