278 citations · 899 across the 26 of their papers we have counts for
11 papers · 1 filter
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…