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