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Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu +3
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid mo…
Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness…
Distributionally Robust Group Backwards Compatibility
Martin Bertran, Natalia Martinez, Alex Oesterling +1
Machine learning models are updated as new data is acquired or new architectures are developed. These updates usually increase model performance, but may introduce backward compati…
Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers
William E. Carson, Dmitry Isaev, Samatha Major +3
Discovering reliable measures that inform on autism spectrum disorder (ASD) diagnosis is critical for providing appropriate and timely treatment for this neurodevelopmental disorde…
Federating for Learning Group Fair Models
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness…
Using Text to Teach Image Retrieval
Haoyu Dong, Ze Wang, Qiang Qiu +1
Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space. Building on the concept of image manifold, we first propose to…