2 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
S Chandra Mouli, Muhammad Ashraful Alam, Bruno Ribeiro
A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require le…
Bias Challenges in Counterfactual Data Augmentation
S Chandra Mouli, Yangze Zhou, Bruno Ribeiro
Deep learning models tend not to be out-of-distribution robust primarily due to their reliance on spurious features to solve the task. Counterfactual data augmentations provide a g…
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
S Chandra Mouli, Bruno Ribeiro
Despite -- or maybe because of -- their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This wo…
Deep Lifetime Clustering
S Chandra Mouli, Leonardo Teixeira, Jennifer Neville +1
The goal of lifetime clustering is to develop an inductive model that maps subjects into clusters according to their underlying (unobserved) lifetime distribution. We introduce…