activity
20132024
most citedPersonalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy

278 citations · 447 across the 23 of their papers we have counts for

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Showing cs.LGShow all

6 papers · 1 filter

cs.LG2021★ 18 cited

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…

cs.LG2019

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…

cs.LG2019★ 7 cited

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…

cs.LG2018

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…

cs.LG2017★ 14 cited

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…

cs.LG2017

Unsupervised Domain Adaptation with Copula Models

Cuong D. Tran, Ognjen Rudovic, Vladimir Pavlovic

We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between t…