14 citations · 15 across the 5 of their papers we have counts for
7 papers
Removing Spurious Correlation from Neural Network Interpretations
Milad Fotouhi, Mohammad Taha Bahadori, Oluwaseyi Feyisetan +2
The existing algorithms for identification of neurons responsible for undesired and harmful behaviors do not consider the effects of confounders such as topic of the conversation.…
Discovering Invariances in Healthcare Neural Networks
Mohammad Taha Bahadori, Layne C. Price
We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately uncha…
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…
Temporal-Clustering Invariance in Irregular Healthcare Time Series
Mohammad Taha Bahadori, Zachary Chase Lipton
Electronic records contain sequences of events, some of which take place all at once in a single visit, and others that are dispersed over multiple visits, each with a different ti…
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak +11
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinic…
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…