research
∙
10/25/2021
Identifying and Benchmarking Natural Out-of-Context Prediction Problems
Deep learning systems frequently fail at out-of-context (OOC) prediction...
share
research
∙
11/12/2020
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
Robustness is of central importance in machine learning and has given ri...
research
∙
06/18/2020
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Standard causal discovery methods must fit a new model whenever they enc...
research
∙
10/21/2019
Detecting Extrapolation with Local Ensembles
We present local ensembles, a method for detecting extrapolation at test...
research
∙
09/18/2019
Causal Modeling for Fairness in Dynamical Systems
In this work, we present causal directed acyclic graphs (DAGs) as a unif...
research
∙
06/06/2019
Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group a...
research
∙
09/07/2018
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data
How do we learn from biased data? Historical datasets often reflect hist...
research
∙
02/17/2018
Learning Adversarially Fair and Transferable Representations
In this work, we advocate for representation learning as the key to miti...
research
∙
11/17/2017
Predict Responsibly: Increasing Fairness by Learning To Defer
Machine learning systems, which are often used for high-stakes decisions...
research
∙
10/20/2016