Search Results for author: Elliot Creager

Found 11 papers, 7 papers with code

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

1 code implementation12 Nov 2020 Robert Adragna, Elliot Creager, David Madras, Richard Zemel

Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving performance on a test distribution distinct from but related to the training distribution.

Causal Discovery Domain Generalization +2

Environment Inference for Invariant Learning

1 code implementation14 Oct 2020 Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness.

Domain Generalization Fairness

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization

no code implementations28 Sep 2020 Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel

Developing learning approaches that are not overly sensitive to the training distribution is central to research on domain- or out-of-distribution generalization, robust optimization and fairness.

Domain Generalization Fairness +1

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

no code implementations ICML 2020 Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier

We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.

Fairness Recommendation Systems

Counterfactual Data Augmentation using Locally Factored Dynamics

1 code implementation NeurIPS 2020 Silviu Pitis, Elliot Creager, Animesh Garg

Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.

Data Augmentation General Reinforcement Learning +2

Causal Modeling for Fairness in Dynamical Systems

1 code implementation ICML 2020 Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel

In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups.


Flexibly Fair Representation Learning by Disentanglement

no code implementations6 Jun 2019 Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes.

Disentanglement Fairness +1

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data

no code implementations7 Sep 2018 David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel

Building on prior work in deep learning and generative modeling, we describe how to learn the parameters of this causal model from observational data alone, even in the presence of unobserved confounders.

Fairness General Classification

Explaining Image Classifiers by Counterfactual Generation

1 code implementation ICLR 2019 Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?

Image Classification

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