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ORCIDElliot Creager
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2020 – today
- 2024
[c15]Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith:
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making. ICML 2024- [c14]Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel:
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. ICML 2024 - [i17]Elliot Creager, Richard S. Zemel:
Online Algorithmic Recourse by Collective Action. CoRR abs/2401.00055 (2024) - 2023
- [c13]Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel:
SurfsUp: Learning Fluid Simulation for Novel Surfaces. ICCV 2023: 14179-14189 - [i16]Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel:
SURFSUP: Learning Fluid Simulation for Novel Surfaces. CoRR abs/2304.06197 (2023) - [i15]Parand Alizadeh Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith:
Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (Preliminary Report). CoRR abs/2312.04772 (2023) - [i14]Elliot Creager:
Robust Machine Learning by Transforming and Augmenting Imperfect Training Data. CoRR abs/2312.12597 (2023) - [i13]Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel:
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR abs/2312.17463 (2023) - 2022
- [c12]Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg:
MoCoDA: Model-based Counterfactual Data Augmentation. NeurIPS 2022 - [i12]Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg:
MoCoDA: Model-based Counterfactual Data Augmentation. CoRR abs/2210.11287 (2022) - 2021
- [c11]Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel:
Environment Inference for Invariant Learning. ICML 2021: 2189-2200 - [c10]Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer:
On Disentangled Representations Learned from Correlated Data. ICML 2021: 10401-10412 - 2020
- [c9]Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel:
Causal Modeling for Fairness In Dynamical Systems. ICML 2020: 2185-2195 - [c8]Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier:
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML 2020: 6987-6998 - [c7]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. NeurIPS 2020 - [i11]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i10]Silviu Pitis, Elliot Creager, Animesh Garg:
Counterfactual Data Augmentation using Locally Factored Dynamics. CoRR abs/2007.02863 (2020) - [i9]Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier:
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR abs/2008.00104 (2020) - [i8]Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel:
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. CoRR abs/2010.07249 (2020) - [i7]Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel:
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR abs/2011.06485 (2020)
2010 – 2019
- 2019
- [c6]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT 2019: 349-358 - [c5]Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud:
Explaining Image Classifiers by Counterfactual Generation. ICLR (Poster) 2019 - [c4]Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. ICML 2019: 1436-1445 - [i6]Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. CoRR abs/1906.02589 (2019) - [i5]Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel:
Causal Modeling for Fairness in Dynamical Systems. CoRR abs/1909.09141 (2019) - 2018
- [c3]Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel:
Gradient-based Optimization of Neural Network Architecture. ICLR (Workshop) 2018 - [c2]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Learning Adversarially Fair and Transferable Representations. ICML 2018: 3381-3390 - [i4]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Learning Adversarially Fair and Transferable Representations. CoRR abs/1802.06309 (2018) - [i3]Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud:
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. CoRR abs/1807.08024 (2018) - [i2]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. CoRR abs/1809.02519 (2018) - 2016
- [c1]Elliot Creager, Noah D. Stein, Roland Badeau, Philippe Depalle:
Nonnegative Tensor Factorization with Frequency Modulation Cues for Blind Audio Source Separation. ISMIR 2016: 211-217 - [i1]Elliot Creager, Noah D. Stein, Roland Badeau, Philippe Depalle:
Nonnegative tensor factorization with frequency modulation cues for blind audio source separation. CoRR abs/1606.00037 (2016)
Coauthor Index
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last updated on 2024-09-04 01:23 CEST by the dblp team
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