278 citations · 899 across the 26 of their papers we have counts for
29 papers
Mixed Effects Random Forests for Personalised Predictions of Clinical Depression Severity
Robert A. Lewis, Asma Ghandeharioun, Szymon Fedor +3
This work demonstrates how mixed effects random forests enable accurate predictions of depression severity using multimodal physiological and digital activity data collected from a…
Predicting Driver Self-Reported Stress by Analyzing the Road Scene
Cristina Bustos, Neska Elhaouij, Albert Sole-Ribalta +3
Several studies have shown the relevance of biosignals in driver stress recognition. In this work, we examine something important that has been less frequently explored: We develop…
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li +3
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued…
Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
Matthew Groh, Ziv Epstein, Chaz Firestone +1
The recent emergence of machine-manipulated media raises an important societal question: how can we know if a video that we watch is real or fake? In two online studies with 15,016…
Personalized Federated Deep Learning for Pain Estimation From Face Images
Ognjen Rudovic, Nicolas Tobis, Sebastian Kaltwang +4
Standard machine learning approaches require centralizing the users' data in one computer or a shared database, which raises data privacy and confidentiality concerns. Therefore, l…
openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion
Felix Weninger, Yue Zhang, Rosalind W. Picard
A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missin…
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias
Asma Ghandeharioun, Brian Eoff, Brendan Jou +1
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task. In this wor…
Hierarchical Reinforcement Learning for Open-Domain Dialog
Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun +2
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals,…
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Daniel Lopez-Martinez, Ke Peng, Arielle Lee +2
Currently self-report pain ratings are the gold standard in clinical pain assessment. However, the development of objective automatic measures of pain could substantially aid pain…
Engineering Music to Slow Breathing and Invite Relaxed Physiology
Grace Leslie, Asma Ghandeharioun, Diane Y. Zhou +1
We engineered an interactive music system that influences a user's breathing rate to induce a relaxation response. This system generates ambient music containing periodic shifts in…
Detection of Real-world Driving-induced Affective State Using Physiological Signals and Multi-view Multi-task Machine Learning
Daniel Lopez-Martinez, Neska El-Haouij, Rosalind Picard
Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing…
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen +5
Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critica…
Tweet Moodifier: Towards giving emotional awareness to Twitter users
Belen Saldias, Rosalind W. Picard
Emotional contagion in online social networks has been of great interest over the past years. Previous studies have focused mainly on finding evidence of affect contagion in homoph…
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Ognjen Rudovic, Meiru Zhang, Bjorn Schuller +1
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and hum…
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques +4
Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation qua…
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Daniel Lopez-Martinez, Patrick Eschenfeldt, Sassan Ostvar +3
Opioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use…
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes
Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero +3
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD…
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals
Daniel Lopez-Martinez, Ke Peng, Sarah C. Steele +3
Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measu…
Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck
Weixuan Chen, Javier Hernandez, Rosalind W. Picard
Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtr…
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)
Yuria Utsumi, Ognjen Rudovic, Kelly Peterson +2
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (A…
Learning via social awareness: Improving a deep generative sketching model with facial feedback
Natasha Jaques, Jennifer McCleary, Jesse Engel +4
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards.…
Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy
Ognjen Rudovic, Jaeryoung Lee, Miles Dai +2
Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to h…
A Trip to the Moon: Personalized Animated Movies for Self-reflection
Fengjiao Peng, Veronica LaBelle, Emily Yue +1
Self-tracking physiological and psychological data poses the challenge of presentation and interpretation. Insightful narratives for self-tracking data can motivate the user toward…
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero +1
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) ba…