2 citations · 4 across the 5 of their papers we have counts for
6 papers
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…
Set Twister for Single-hop Node Classification
Yangze Zhou, Vinayak Rao, Bruno Ribeiro
Node classification is a central task in relational learning, with the current state-of-the-art hinging on two key principles: (i) predictions are permutation-invariant to the orde…
Contextual Unsupervised Outlier Detection in Sequences
Mohamed A. Zahran, Leonardo Teixeira, Vinayak Rao +1
This work proposes an unsupervised learning framework for trajectory (sequence) outlier detection that combines ranking tests with user sequence models. The overall framework ident…
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…
Typed Linear Algebra for Efficient Analytical Querying
João M. Afonso, Gabriel D. Fernandes, João P. Fernandes +5
This paper uses typed linear algebra (LA) to represent data and perform analytical querying in a single, unified framework. The typed approach offers strong type checking (as in mo…