1 citations · 1 across the 2 of their papers we have counts for
5 papers
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Biagio Brattoli, Joseph Tighe, Fedor Zhdanov +2
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL…
Causal Regularization
Dominik Janzing
I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample r…
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when $P(X \mid Y, Z) = P…
Causal Regularization
Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi +3
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to st…
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data
Krzysztof Chalupka, Tobias Bischoff, Pietro Perona +1
We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 20…