Elliot Creager

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Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
David MadrasUniversity of TorontoVerified email at cs.toronto.edu
Jörn-Henrik JacobsenApple AI/ML ResearchVerified email at vectorinstitute.ai
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of TorontoVerified email at utoronto.ca
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Chun-Hao ChangMetaVerified email at cs.toronto.edu
Marissa A. WeisUniversity of GöttingenVerified email at uni-goettingen.de
Silviu PitisUniversity of Toronto, Vector InstituteVerified email at cs.toronto.edu
Animesh GargGeorgia Institute of Technology, University of Toronto, NvidiaVerified email at gatech.edu
Frederik TräubleMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Francesco LocatelloAssistant Professor, ISTAVerified email at ista.ac.at
Stefan BauerHelmholtz | TUM | CIFARVerified email at tum.de
Niki KilbertusTechnical University of Munich & Helmholtz MunichVerified email at tum.de
Anirudh GoyalGoogle DeepMind, Mila, Université de MontréalVerified email at umontreal.ca
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Martin MladenovGoogle ResearchVerified email at google.com
Craig BoutilierPrincipal Scientist, GoogleVerified email at google.com
Omer Ben-PoratAssistant Professor, Technion—Israel Institute of TechnologyVerified email at technion.ac.il
Will GrathwohlResearch Scientist, DeepmindVerified email at deepmind.com

Elliot Creager

University of Waterloo

Verified email at uwaterloo.ca - Homepage

Machine Learning


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Learning adversarially fair and transferable representations D Madras, E Creager, T Pitassi, R Zemel International Conference on Machine Learning, 3384-3393, 2018	751	2018
Flexibly fair representation learning by disentanglement E Creager, D Madras, JH Jacobsen, M Weis, K Swersky, T Pitassi, ... International conference on machine learning, 1436-1445, 2019	393	2019
Environment Inference for Invariant Learning E Creager, JH Jacobsen, R Zemel ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning, 2020	372	2020
Explaining Image Classifiers by Counterfactual Generation CH Chang, E Creager, A Goldenberg, D Duvenaud arXiv preprint arXiv:1807.08024, 2018	298	2018
Fairness through causal awareness: Learning causal latent-variable models for biased data D Madras, E Creager, T Pitassi, R Zemel Proceedings of the conference on fairness, accountability, and transparency …, 2019	172	2019
On disentangled representations learned from correlated data F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ... International conference on machine learning, 10401-10412, 2021	126	2021
Counterfactual data augmentation using locally factored dynamics S Pitis, E Creager, A Garg Advances in Neural Information Processing Systems 33, 3976-3990, 2020	90	2020
Causal modeling for fairness in dynamical systems E Creager, D Madras, T Pitassi, R Zemel International conference on machine learning, 2185-2195, 2020	77	2020
Optimizing long-term social welfare in recommender systems: A constrained matching approach M Mladenov, E Creager, O Ben-Porat, K Swersky, R Zemel, C Boutilier International Conference on Machine Learning, 6987-6998, 2020	64	2020
Fairness and robustness in invariant learning: A case study in toxicity classification R Adragna, E Creager, D Madras, R Zemel arXiv preprint arXiv:2011.06485, 2020	48	2020
Mocoda: Model-based counterfactual data augmentation S Pitis, E Creager, A Mandlekar, A Garg Advances in Neural Information Processing Systems 35, 18143-18156, 2022	30	2022
Interpreting neural network classifications with variational dropout saliency maps CH Chang, E Creager, A Goldenberg, D Duvenaud Proc. NIPS 1 (2), 1-9, 2017	16	2017
Gradient-based optimization of neural network architecture W Grathwohl, E Creager, SKS Ghasemipour, R Zemel	15	2018
Nonnegative tensor factorization with frequency modulation cues for blind audio source separation E Creager, ND Stein, R Badeau, P Depalle arXiv preprint arXiv:1606.00037, 2016	6	2016
Online Algorithmic Recourse by Collective Action E Creager, R Zemel ICML 2021 Workshop on Algorithmic Recourse, 2021	4	2021
Musical source separation by coherent frequency modulation cues E Creager McGill University (Canada), 2015	3	2015
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift B Eyre, E Creager, D Madras, V Papyan, R Zemel	1	2023
SURFSUP: Learning Fluid Simulation for Novel Surfaces A Mani, IP Chandratreya, E Creager, C Vondrick, R Zemel Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023	1	2023
Towards Environment-Invariant Representation Learning for Robust Task Transfer B Eyre, R Zemel, E Creager ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 2022	1	2022
Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making PA Alamdari, TQ Klassen, E Creager, SA McIlraith arXiv preprint arXiv:2312.04772, 2023		2023

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