Elliot Creager

According to our database, Elliot Creager authored at least 23 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Online Algorithmic Recourse by Collective Action.

Elliot Creager, Richard S. Zemel

CoRR, 2024

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift.

Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making.

Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Robust Machine Learning by Transforming and Augmenting Imperfect Training Data.

Elliot Creager

CoRR, 2023

Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (Preliminary Report).

Parand Alizadeh Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith

CoRR, 2023

SurfsUp: Learning Fluid Simulation for Novel Surfaces.

Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

MoCoDA: Model-based Counterfactual Data Augmentation.

Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

On Disentangled Representations Learned from Correlated Data.

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer

Proceedings of the 38th International Conference on Machine Learning, 2021

Environment Inference for Invariant Learning.

Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel

CoRR, 2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization.

Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel

CoRR, 2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.

Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer

CoRR, 2020

Counterfactual Data Augmentation using Locally Factored Dynamics.

Silviu Pitis, Elliot Creager, Animesh Garg

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier

Proceedings of the 37th International Conference on Machine Learning, 2020

Causal Modeling for Fairness In Dynamical Systems.

Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Flexibly Fair Representation Learning by Disentanglement.

Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel

Proceedings of the 36th International Conference on Machine Learning, 2019

Explaining Image Classifiers by Counterfactual Generation.

Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

Proceedings of the 7th International Conference on Learning Representations, 2019

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

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

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

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

CoRR, 2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling.

Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

CoRR, 2018

Learning Adversarially Fair and Transferable Representations.

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

Proceedings of the 35th International Conference on Machine Learning, 2018

Gradient-based Optimization of Neural Network Architecture.

Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Nonnegative Tensor Factorization with Frequency Modulation Cues for Blind Audio Source Separation.

Elliot Creager, Noah D. Stein, Roland Badeau, Philippe Depalle

Proceedings of the 17th International Society for Music Information Retrieval Conference, 2016