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Elliot Creager
Elliot Creager
University of Waterloo
在 uwaterloo.ca 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Learning adversarially fair and transferable representations
D Madras, E Creager, T Pitassi, R Zemel
International Conference on Machine Learning, 3384-3393, 2018
7042018
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
3592019
Environment Inference for Invariant Learning
E Creager, JH Jacobsen, R Zemel
ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning, 2020
3342020
Explaining Image Classifiers by Counterfactual Generation
CH Chang, E Creager, A Goldenberg, D Duvenaud
arXiv preprint arXiv:1807.08024, 2018
2812018
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
1592019
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
1172021
Counterfactual data augmentation using locally factored dynamics
S Pitis, E Creager, A Garg
Advances in Neural Information Processing Systems 33, 3976-3990, 2020
772020
Causal modeling for fairness in dynamical systems
E Creager, D Madras, T Pitassi, R Zemel
International conference on machine learning, 2185-2195, 2020
692020
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
622020
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
462020
Mocoda: Model-based counterfactual data augmentation
S Pitis, E Creager, A Mandlekar, A Garg
Advances in Neural Information Processing Systems 35, 18143-18156, 2022
222022
Interpreting neural network classifications with variational dropout saliency maps
CH Chang, E Creager, A Goldenberg, D Duvenaud
Proc. NIPS 1 (2), 1-9, 2017
162017
Gradient-based optimization of neural network architecture
W Grathwohl, E Creager, SKS Ghasemipour, R Zemel
152018
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
62016
Online Algorithmic Recourse by Collective Action
E Creager, R Zemel
ICML 2021 Workshop on Algorithmic Recourse, 2021
42021
Musical source separation by coherent frequency modulation cues
E Creager
McGill University (Canada), 2015
32015
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
B Eyre, E Creager, D Madras, V Papyan, R Zemel
12023
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
12022
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
Robust Machine Learning by Transforming and Augmenting Imperfect Training Data
E Creager
University of Toronto (Canada), 2023
2023
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