17 citations · 25 across the 7 of their papers we have counts for
4 papers · 1 filter
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Nikhil Mehta, Kevin J Liang, Jing Huang +3
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored. Such f…
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta +2
As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critica…
WAFFLe: Weight Anonymized Factorization for Federated Learning
Weituo Hao, Nikhil Mehta, Kevin J Liang +3
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. In light of…
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin, Piyush Rai
Stochastic blockmodels (SBM) and their variants, , mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have…