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Natasha Jaques
Social learning helps humans and animals rapidly adapt to new circumstances, and drives the emergence of complex learned behaviors. My research is focused on Social Reinforcement Learning—developing algorithms that combine insights from social learning and multi-agent training to improve AI agents’ learning, generalization, coordination, and human-AI interaction.
I am an Assistant Professor at the University of Washington Paul G. Allen School of Computer Science & Engineering, where I lead the Social RL Lab. I am also a Senior Research Scientist at Google DeepMind. If you are interested in joining my lab as a PhD student, check out our contact page for more information.
During my PhD at MIT, I developed techniques for fine-tuning language models with RL and learning from human feedback which were later built on by OpenAI’s series of work on Reinforcement Learning from Human Feedback (RLHF) [1,2,3]. In the multi-agent space, I developed techniques for improving coordination through the optimization of social influence. I interned at DeepMind, Google Brain, and was an OpenAI Scholars Mentor. I was subsequently a Visiting Postdoctoral Scholar at UC Berkeley, in Sergey Levine’s group, and a Senior Research Scientist at Google Brain, where I built novel methods for adversarial environment generation to improve the robustness of RL agents. My work has received various awards, including Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, and the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. My work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine, and on CBC radio, among others. I earned a Master's degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.
Download my CV.Interests
Education
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PhD in the Media Lab, 2019
Massachusetts Institute of Technology
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MSc in Computer Science, 2014
University of British Columbia
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BSc in Computer Science, 2012
University of Regina
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BA in Psychology, 2012
University of Regina
Selected Awards
- 2023 Best Paper at the AAAI Representation Learning for Responsible Human-Centric AI workshop
- 2021 Outstanding PhD Dissertation from the international Association for the Advancement of Affective Computing
- 2021 Best of Collection in the journal IEEE Transactions on Affective Computing (impact factor: 10.5)
- 2020 Best Paper at the NeurIPS Workshop on Cooperative AI
- 2019 Best Paper Honourable Mention at the International Conference on Machine Learning (ICML) 2019
- 2019 Rising Stars in EECS Pitch Competition Winner
- 2019 Best Paper Nominee at the NeurIPS Workshop on Conversational AI
- 2017 Centennial Alumni of Distinction at Campion College
- 2016 Best Paper at the NeurIPS Workshop on ML for Healthcare
- 2016 Best Demo at Neural Information Processing Systems (NeurIPS)
Selected Press
- Degrees Magazine. Cataldo, S. (2021, November 19). The sky’s the limit.
- Science. Hutson, M. (2021, January 19). Who needs a teacher? Artificial intelligence designs lesson plans for itself.
- IEEE Spectrum. Hutson, M. (2019, June 17). DeepMind Teaches AI Teamwork.
- MIT Technology Review. Hao, K. (2019, June 20). Here are 10 ways AI could help fight climate change.
- National Geographic. Snow, J. (2019, July 18). How artificial intelligence can tackle climate change.
- Quartz. Gershgorn, D. (2018, February 16). Google is building AI to make humans smile.
- MIT Technology Review. Knight, W. (2016, November 30). AI songsmith cranks out surprisingly catchy tunes.
- Boston Magazine. Annear, S. (2015, January 5). Website tracks your happiness to remind you life’s not so bad.
- CBC radio. Brace, S. (2015, January 5). Regina woman develops smile app at MIT.
Featured Publications
Michael Dennis*, Natasha Jaques*, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine
2020
In Neural Information Processing Systems (NeurIPS) Oral (top 1% of submissions)
PAIRED trains an agent to generate environments that maximize regret between a pair of learning agents. This creates feasible yet challenging environments, which exploit weaknesses in the agents to make them more robust. PAIRED significantly improves generalization to novel tasks.
Natasha Jaques*, J. H. Shen*, A. Ghandeharioun, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, R. Picard
2020
In Empirical Methods in Natural Language Processing (EMNLP)
We train dialog models with interactive data from conversations with real humans, using a novel Offline RL technique based on KL-control. Rather than rely on manual ratings, we learn from implicit signals like sentiment, and show that this results in better performance.
Natasha Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. A. Ortega, D. J. Strouse, J.Z. Leibo, N. de Freitas
2019
In International Conference on Machine Learning (ICML) Best Paper Honourable Mention (top 0.26% of submissions)
Social influence is a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning, through rewarding agents for having causal influence over other agents’ actions, thus increasing mutual information between agents’ actions. Optimizing for influence leads to agents learning emergent communication protocols. Unlike prior work, influence can be computed in a fully decentralized manner.
Natasha Jaques*, S. Taylor*, E. Nosakhare, A. Sano, R. Picard
2017
In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper
Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.
Publications
To find relevant content, try searching publications, filtering using the buttons below, or exploring popular topics. A * denotes equal contribution.
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Interpreting whether multi-agent reinforcement learning (MARL) agents have successfully learned to coordinate with each other, versus finding some other way to exploit the reward function, is a longstanding problem. We develop a novel interpretability method for MARL based on concept bottlenecks, which enables detecting which agents are truly coordinating, which environments require coordination, and identifying lazy agents.
N. Grupen, Natasha Jaques, B. Kim, S. Omidshafiei
2022
In Preprint
Moral Foundations theory decomposes human moral reasoning into five factors, which vary reliably across different human populations and political affiliations. We use moral foundations to analyze large language models like GPT-3 to determine what, if any, consistent moral values it brings to conversations, whether these can be deliberately manipulated, and whether holding a particular moral stance affects downstream tasks.
M. Abdulhai, C. Crepy, D. Valter, J. Canny, S. Levine, Natasha Jaques
2022
In Preprint
Past work on curriculum generation in RL has focused on training a teacher agent to generate tasks for a student agent that accelerate student learning and improve generalization. In this work, we create a mathematical framework that formalizes these concepts and subsumes prior work, taking inspiration from the psychological concept of the Zone of Proximal Development. We propose two new techniques based on rejection sampling and maximizing the student’s gradient norm that improve curriculum learning.
R. E. Wang, J. Mu, D. Arumugam, Natasha Jaques, N. Goodman
2022
In Preprint
Reinforement Learning can potentially be a powerful tool for solving complex combinatorial optimization problems, such as microprocessor desgin. Here, we show that a multi-agent RL approach outperforms past work using single agent RL, since the problem can easily be decomposed into designing independent sub-systems.
S. Krishnan, Natasha Jaques, S. Omidshafiei, D. Zhang, I. Gur, V. J. Reddi, S. Faust
2022
In Preprint
Using inverse reinforcement learning to infer human preferences is challenging, because it is an underspecified problem. We use multi-task RL pre-training and successor features to learn a strong prior over the space of reasonable goals in an environment—which we call a basis—that enables rapidly inferring an expert’s reward function in only 100 samples.
M. Abdulhai, Natasha Jaques, S. Levine
2022
In Preprint
We study the automatic generation of natural language navigation instructions in visually realistic indoor environments. Existing generators suffer from poor visual grounding, skip steps, and hallucinate objects. We address this using a large language model which incorporates visual landmark detection.. The model dramatically increases the quality of generated instructions, such that humans can follow them with a 71% success rate (SR); just shy of the 75% SR of real human instructions.
S. Wang, C. Montgomery, J. Orbay, V. Birodkar, A. Faust, I. Gur, Natasha Jaques, A. Waters, J. Baldridge, P. Anderson
2022
In Computer Vision and Pattern Recognition (CVPR)
Physiological signals like heart rate and skin conductance collected from wearable devices open up a range of interesting research for clinicians and psychologists, including studying physiological reactivity to daily events and stressors. We introduce a new R package and application for analyzing wearable physiological data which enables large scale processing, and ease of use in gaining insight into this data.
P. de Looff, R. Duursma, Noordzij. Noordzi, S. Taylor, Natasha Jaques, F. Scheepers, K. De Schepper, S. Koldijk
2022
In Frontiers in behavioral neuroscience
We analyze and improve upon PAIRED in the case of learning to generate challenging compositional tasks. We apply our improved algorithm to the complex task of training RL agents to navigate websites, and find that it is able to generating a challenging curriculum of novel sites. We achieve a 4x improvement over the strongest web navigation baselines, and deploy our model to navigate real-world websites..
I. Gur, Natasha Jaques, K. Malta, M. Tiwari, H. Lee, A. Faust
2021
In Neural Information Processing Systems (NeurIPS)
PsiPhi-Learning learns successor representations for the policies of other agents and the ego agent, using a shared underlying state representation. Learning from other agents helps the agent take better actions at inference time, and learning from RL experience improves modeling of other agents.
A. Filos, C. Lyle, Y. Gal, S. Levine, Natasha Jaques*, G. Farquhar*
2021
In International Conference on Machine Learning (ICML) Oral (top 3% of submissions)
Model-free RL agents fail to learn from experts present in multi-agent environments. By adding a model-based auxiliary loss, we induce social learning, which allows agents to learn how to learn from experts. When deployed to novel environments with new experts, they use social learning to determine how to solve the task, and generalize better than agents trained alone with RL or imitation learning.
Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques
2021
In International Conference on Machine Learning (ICML); NeurIPS Cooperative AI Workshop Best Paper
Adversarial Surprise creates a competitive game between an Expore policy and a Control policy, which fight to maximize and minimize the amount of entropy an RL agent experiences. We show both theoretically and empirically that this technique fully explores the state space of partially-observed, stochastic environments.
A. Fickinger*, Natasha Jaques*, S. Parajuli, M. Chang, N. Rhinehart, G. Berseth, S. Russell, S. Levine
2021
In ICML Unsupervised Reinforcement Learning workshop
Joint attention is a critical component of human social cognition. In this paper, we ask whether a mechanism based on shared visual attention can be useful for improving multi-agent coordination and social learning.
D. Lee, Natasha Jaques, J. Kew, D. Eck, D. Schuurmans, A. Faust
2021
In ICRA Social Intelligence Workshop Spotlight talk
PAIRED trains an agent to generate environments that maximize regret between a pair of learning agents. This creates feasible yet challenging environments, which exploit weaknesses in the agents to make them more robust. PAIRED significantly improves generalization to novel tasks.
Michael Dennis*, Natasha Jaques*, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine
2020
In Neural Information Processing Systems (NeurIPS) Oral (top 1% of submissions)
We train dialog models with interactive data from conversations with real humans, using a novel Offline RL technique based on KL-control. Rather than rely on manual ratings, we learn from implicit signals like sentiment, and show that this results in better performance.
Natasha Jaques*, J. H. Shen*, A. Ghandeharioun, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, R. Picard
2020
In Empirical Methods in Natural Language Processing (EMNLP)
Existing metrics for automatically evaluating dialog models correlate poorly with human judgements, and are evaluated on static conversation snippets. Instead, we deploy bots to interact live with humans, then approximate human ratings with state-of-the-art accuracy using conversations generated with self-play.
A. Ghandeharioun*, J. H. Shen*, Natasha Jaques*, C. Ferguson, N. Jones, A. Lapedriza, R. Picard
2019
In Neural Information Processing Systems (NeurIPS)
To predict which users are at risk of suicide based on a small dataset of online posts, we leverage pre-trained sentence embeddings from large language models, and achieve high F1 scores (.83-.92). We further analyze users’ posts to determine which topics are most associated with suicidal users.
N. Jones, Natasha Jaques, P. Pataranutaporn, A. Ghandeharioun, R. Picard
2019
In Affective Computing and Intelligence Interaction (ACII) workshop on Machine Learning for Mental Health
For the first time, we use hierarchical reinforcement learning to train open-domain dialog models, enabling the optimization of long-term, conversational, rewards, including reducing the toxicity of generated language. Our approach provides significant improvements over state-of-the-art dialog models.
A. Saleh*, Natasha Jaques*, A. Ghandeharioun, J. H. Shen, R. Picard
2019
In Association for the Advancement of Artificial Intelligence (AAAI) Oral (top 7.8% of submissions)
My PhD Thesis spans both Social Reinforcement Learning and Affective Computing, investigating how affective and social intelligence can enhance machine learning algorithms, and how machine learning can enhance our ability to predict and understand human affective and social phenomena.
Natasha Jaques
2019
In Massachusetts Institute of Technology
Social influence is a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning, through rewarding agents for having causal influence over other agents’ actions, thus increasing mutual information between agents’ actions. Optimizing for influence leads to agents learning emergent communication protocols. Unlike prior work, influence can be computed in a fully decentralized manner.
Natasha Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. A. Ortega, D. J. Strouse, J.Z. Leibo, N. de Freitas
2019
In International Conference on Machine Learning (ICML) Best Paper Honourable Mention (top 0.26% of submissions)
This paper comprehensively surveys the ways in which machine learning could be usefully deployed in the fight against climate change. From smart grids to disaster management, we identify high impact problems and outline how machine learning can be employed to address them.
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
In ACM Computing Surveys
We show the outputs of a generative model of sketches to human observers and record their facial expressions. Using only a small number of facial expression samples, we are able to tune the model to produce drawings that are significantly better rated by humans.
Natasha Jaques, J. McCleary, J. Engel, D. Ha, F. Bertsch, D. Eck, R. Picard
2018
In International Conference on Learning Representations (ICLR) workshop
During a zero-gravity parabolic flight, we recorded participants’ heart rate, accelerometer, and skin conductance measurements as well as their self-report nausea, anxiety, and excitement. Statistical analysis revealed that skin conductance is predictive of nausea, while heart rate is predictive of anxiety and excitement.
K. Johnson, S. Taylor, S. Fedor, Natasha Jaques, W. Chen, R. Picard
2018
In IEEE Engineering in Medicine and Biology Society (EMBC)
We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.
S. Taylor, Natasha Jaques, Sano, A. E. Nosakhare, E. B. Klerman, R. Picard
2017
In Journal of Sleep and Sleep Disorders Research (suppl_1)
Predicting signals like stress and health depends on collecting noisy data from a number of modalities, e.g. smartphone data, or physiological data from a wrist-worn sensor. Our method can continue making accurate predictions even when a modality goes missing; for example, if the person forgets to wear their sensor.
Natasha Jaques, S. Taylor, A. Sano, R. Picard
2017
In International Conference on Affective Computing and Intelligent Interaction (ACII)
Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.
Natasha Jaques*, S. Taylor*, E. Nosakhare, A. Sano, R. Picard
2017
In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper
Modeling measures like mood, stress, and health using a monolithic machine learning model leads to low prediction accuracy. Instead, we develop personalized regression models using multi-task learning and Gaussian Processes, leading to dramatic improvements in next-day predictions.
Natasha Jaques, O. Rudovic, S. Taylor, A. Sano, R. Picard
2017
In Proceedings of Machine Learning Research
To combine supervised learning on data with reinforcement learning, we pre-train a supervised data prior, and penalize KL-divergence from this model using RL training. This enables effective learning of complex sequence-modeling problems for which we wish to match the data while optimizing external metrics like drug effectiveness. The approach produces compelling results in the disparate domains of music generation and drug discovery.
Natasha Jaques, S. Gu, D. Bahdanau, J. M. Hernandez-Lobato, R. E. Turner, D. Eck
2017
In International Conference on Machine Learning (ICML)
To help promote behavior change, we leverage the power of social obligation, and conduct an experiment in which participants are paired together and asked to design a Behavioral Intervention Technology (BIT) customized to suit their partner’s behavior change goal.
Natasha Jaques, T. Rich, K. Dinakar, N. Farve, W.V. Chen, P. Maes, R. Picard
2016
In Proceedings of the CHI Conference Extended Abstracts on Human Factors
This demo deployed RL Tuner and other Magenta music generation models into an interactive interface in which users can collaborate creatively with a machine learning model. The interface supports call and response interaction, automatically generating an accompaniment to the user’s melody, or melody morphing: responding both with variations on the user’s melody and a bass accompaniment.
A. Roberts, J. Engel, C. Hawthorne, I. Simon, E. Waite, S. Oore, Natasha Jaques, C. Resnick, D. Eck
2016
In Neural Information Processing Systems (NeurIPS) Best Demo
Machine learning applied to nightly data from sensors and smartphones, shows value for predicting college student’s mood the following evening. Using multi-task learning to simultaneously predicted related wellbeing factors like health, energy, stress, and alertness improves performance.
S. Taylor, Natasha Jaques, A. Sano, A. Azaria, A. Ghandeharioun, R. Picard
2016
In Sleep
We collect observational data from real human conversations, and develop a measure of how much participants experienced bonding or chemistry. We analyze the effects of personality and attitudes on bonding, and find that attentiveness and excitement are more effective at promoting bonding than traits like attractiveness and humour.
Natasha Jaques, Y. K. Kim, \& Picard R. Picard R
2016
In Intelligent Virtual Agents (IVA)
Generating music using traditional supervised sequence models suffers from known failure modes, including the inability to produce coherent global structure. Music is an interesting sequence generation problem, because musical compositions adhere to known rules. We impose these rules with a novel algorithm combining RL and supervised learning.
Natasha Jaques, S. Gu, R. E. Turner, D. Eck
2016
In International Conference on Learning Representations (ICLR) - workshop
Given only one-minute slices of facial expressions and body language, we use machine learning to accurately predict whether two humans having a conversation will bond with each other. We analyze factors which lead to bonding and discover that synchrony in body language and appropriate, empathetic facial expressions lead to higher bonding.
Natasha Jaques, D. McDuff, Y. K. Kim, \& Picard R. Picard R
2016
In Intelligent Virtual Agents (IVA)
We use labels provided by domain experts to classify whether artifacts are present in an Electrodermal Activity signal. Through the use of active learning, we improve sample efficiency and reduce the burden on human experts by as much as 84%, while offering the same or improved performance.
V. Xia, Natasha Jaques, S. Taylor, S. Fedor, R. Picard
2015
In IEEE Conference on Signal Processing in Medicine and Biology (SPMB)
Ambulatory measurement of Electrodermal Activity (EDA) from the wrist has important clinical benefits, such as predicting mood, stress, health, or even seizures. However, ambulatory measurement is noisy, and artifacts can easily be mistaken for true Skin Conductance Responses (SCRs). In addition to our paper which describes a machine learning method for detecting artifacts with 95% test accuracy, we built EDA Explorer, an open-source tool that allows users to automatically detect artifacts and SCRs within their data.
S. Taylor*, Natasha Jaques*, W. Chen, S. Fedor, A. Sano, R. Picard
2015
In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
The Challenge is a tool aimed at promoting social connections and decreasing sedentary activity in a workplace environment. Participants are paired with a partner to complete short physical challenges, leveraging social obligation and social consensus to drive behavior change.
Natasha Jaques, N. Farve
2015
In International Conference on Persuasive Technologies
Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.
Natasha Jaques*, S. Taylor*, A. Sano, R. Picard
2015
In Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning
We train machine learning models to predict students’ happiness from extensive data comprising physiological signals, location, smartphone logs, and behavioral questions. Analyzing which features provide the highest information gain reveals that skin conductance during sleep, social interaction, exercise, and fewer phone screen hours are all positively associated with happiness.
Natasha Jaques*, S. Taylor*, A. Azaria, A. Ghandeharioun, A. Sano, R. Picard
2015
In International Conference on Affective Computing and Intelligent Interaction (ACII)
We trained machine learning models to classify happy vs. sad moods in college students using data from surveys and wearable sensors. Factors such as poor health-related behavior, more academic activity hours, and more neutral social interactions were highly predictive of mood.
A. Sano, A. Z. Yu, A. W. McHill, A.J. Phillips, S. Taylor, Natasha Jaques, C. A. Czeisler, E. B. Klerman, R. Picard
2015
In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
SNAPSHOT was a large-scale study of college undergraduates which tracked detailed longitudinal data from smartphones, wearable sensors, behavioral data, and mental health and sleep quality surveys. This initial study analyzed relationships between sleep quality, stress, and GPA, and used machine learning to predict these indices from objective phone and sensor data.
A. Sano, A. J. Phillips, A. Z. Yu, A. W. McHill, S. Taylor, Natasha Jaques, C. A. Czeisler, E. B. Klerman, R. Picard
2015
In Wearable and Implantable Body Sensor Networks (BSN)
SmileTracker is an app that uses facial expression recognition to take a screenshot of the user’s screen whenever they smile. The screenshot and image of the user’s face are saved, to help them remember positive content they encountered during the day. Users can opt to share their images to a public gallery.
Natasha Jaques, W. V. Chen, R. Picard
2015
In Proceedings of the CHI Conference Extended Abstracts
We propose a method for removing motion artifacts from Electrodermal Activity using a stationary wavelet transform. We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level and skin conductance responses. Our method achieves a greater reduction of artifacts while retaining motion-artifact-free data.
W. Chen, Natasha Jaques, S. Taylor, A. Sano, S. Fedor, \& Picard R. Picard R
2015
In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Using eye-tracking data collected while students interact with an Intelligent Tutoring System, we train machine learning models to predict when students are experiencing boredom and curiosity. Which analyze which features are most relevant to detecting when students are engaged and curious vs. disengaged and bored.
Natasha Jaques, C. Conati, J. M. Harley, R. Azevedo
2014
In Intelligent Tutoring Systems
We conduct a study in which participants form American Sign Language hand signs while being recorded with a Microsoft Kinect. The resulting infra-red distance data are used to train both neural networks with dropout (dropout NN) and Random Forests; dropout NN perform significantly better.
Natasha Jaques, J. Nutini
2013
In Unpublished manuscript
This paper investigates the utility of using the Fast Johnson-Lindenstrauss Transform to produce a low-dimensional random projection of eye-tracking data features that can be used for classifying emotion in an Intelligent Tutoring System. Interestingly, the FJLT provides similar or superior performance to more computationally expensive techniques.
Natasha Jaques
2013
In Unpublished manuscript
This study uses eye tracking to assess how students interact with automatic, adaptive hints in an Intelligent Tutoring System. Specifically, we study Prime Climb, an educational game which provides individualized support for learning number factorization skills in the form of hints generated from a model of student learning.
C. Conati, Natasha Jaques, M. Muir
2013
In International Journal of Artificial Intelligence in Education
Popular Topics
Affective Computing
Communication and Language
Cooperation
Deep Learning
Electrodermal Activity
Emergent Complexity
Generalization
Generative Models
Healthcare
Human-AI Interaction
Human-Computer Interaction
Intelligent Tutoring Systems
Machine Learning
Multi-Agent
Physiology
Reinforcement Learning
Sensors
Sequence Modeling
Social Learning
Wellbeing
Featured Talks
Research Communities
- Together with Eugene Vinitsky, I run the Berkeley Multi-Agent Reinforcement Learning Seminar, which includes participants from Berkeley, Stanford, Google Brain, OpenAI, Facebook AI Research (FAIR), and other universities.
- Co-organizer of the NeurIPS 2021 Cooperative AI workshop.
- As part of the workshop, I planned and implented a mentorship program to provide feedback on submissions to students from underrepresented groups.
- Panelist and moderator for the NeurIPS 2020 Cooperative AI workshop.
- Co-organizer of the ICLR 2020 Climate Change for Artificial Intelligence (CCAI) workshop.
- Former Social Media Lead for CCAI.
- Co-organizer of the NeurIPS 2019 Emergent Communication (EmeComm) workshop.
- Co-organizer of the ICML 2018 Artificial Intelligence in Affective Computing (AffComp) workshop.