Elliot Creager

According to our database1, Elliot Creager authored at least 23 papers between 2016 and 2024.

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Bibliography

2024
Online Algorithmic Recourse by Collective Action.
CoRR, 2024

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Robust Machine Learning by Transforming and Augmenting Imperfect Training Data.
CoRR, 2023

Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (Preliminary Report).
CoRR, 2023

SurfsUp: Learning Fluid Simulation for Novel Surfaces.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
MoCoDA: Model-based Counterfactual Data Augmentation.
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.
Proceedings of the 38th International Conference on Machine Learning, 2021

Environment Inference for Invariant Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification.
CoRR, 2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization.
CoRR, 2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.
CoRR, 2020

Counterfactual Data Augmentation using Locally Factored Dynamics.
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.
Proceedings of the 37th International Conference on Machine Learning, 2020

Causal Modeling for Fairness In Dynamical Systems.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Flexibly Fair Representation Learning by Disentanglement.
Proceedings of the 36th International Conference on Machine Learning, 2019

Explaining Image Classifiers by Counterfactual Generation.
Proceedings of the 7th International Conference on Learning Representations, 2019

Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data.
CoRR, 2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling.
CoRR, 2018

Learning Adversarially Fair and Transferable Representations.
Proceedings of the 35th International Conference on Machine Learning, 2018

Gradient-based Optimization of Neural Network Architecture.
Proceedings of the 6th International Conference on Learning Representations, 2018

2016
Nonnegative Tensor Factorization with Frequency Modulation Cues for Blind Audio Source Separation.
Proceedings of the 17th International Society for Music Information Retrieval Conference, 2016


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