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03/29/2022
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets
Pooling multiple neuroimaging datasets across institutions often enables...
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02/19/2022
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Uncertainty estimation in deep models is essential in many real-world ap...
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01/10/2022
Towards Group Robustness in the presence of Partial Group Labels
Learning invariant representations is an important requirement when trai...
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02/16/2021
Learning Invariant Representations using Inverse Contrastive Loss
Learning invariant representations is a critical first step in a number ...
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04/03/2020
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret
Algorithmic decision making based on computer vision and machine learnin...
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10/10/2019
Active Learning with Importance Sampling
We consider an active learning setting where the algorithm has access to...
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09/12/2019
Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently
Rectified Linear Units (ReLUs) are among the most widely used activation...
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09/12/2019