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Natasha Jaques
Researcher at University of California, Berkeley
Publications - 54
Citations - 2895
Natasha Jaques is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 21, co-authored 48 publications receiving 1798 citations. Previous affiliations of Natasha Jaques include Google & Massachusetts Institute of Technology.
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Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
Proceedings Article
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques,Angeliki Lazaridou,Edward Hughes,Caglar Gulcehre,Pedro A. Ortega,DJ Strouse,Joel Z. Leibo,Nando de Freitas +7 more
TL;DR: Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols.
Posted Content
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog.
Natasha Jaques,Asma Ghandeharioun,Judy Hanwen Shen,Craig Ferguson,Agata Lapedriza,Noah Jones,Shixiang Gu,Rosalind W. Picard +7 more
TL;DR: This work develops a novel class of off-policy batch RL algorithms, able to effectively learn offline, without exploring, from a fixed batch of human interaction data, using models pre-trained on data as a strong prior, and uses KL-control to penalize divergence from this prior during RL training.
Proceedings ArticleDOI
Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
Akane Sano,Andrew J. K. Phillips,Amy Z. Yu,Andrew W. McHill,Sara Taylor,Natasha Jaques,Charles A. Czeisler,Elizabeth B. Klerman,Rosalind W. Picard +8 more
TL;DR: This work analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data.
Proceedings ArticleDOI
Automatic identification of artifacts in electrodermal activity data
TL;DR: The development of a machine learning algorithm for automatically detecting EDA artifacts is described, and an empirical evaluation of classification performance is provided.