2 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms
Mohsen Minaei, S Chandra Mouli, Mainack Mondal +2
Over-sharing poorly-worded thoughts and personal information is prevalent on online social platforms. In many of these cases, users regret posting such content. To retrospectively…
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…
Identifying User Survival Types via Clustering of Censored Social Network Data
S Chandra Mouli, Abhishek Naik, Bruno Ribeiro +1
The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily…