Elliot Creager

selected publications

1. ICML

   Environment Inference for Invariant Learning

   Elliot Creager, Joern-Henrik Jacobsen, and Richard Zemel

   In Proceedings of the 38th International Conference on Machine Learning Jul 2021

   Abs arXiv Bib PDF Code

   Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into “domains” or “environments”. Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds dataset. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.

   @inproceedings{creager_environment_2021,
     title = {Environment {Inference} for {Invariant} {Learning}},
     language = {en},
     urldate = {2022-09-07},
     booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}},
     publisher = {PMLR},
     author = {Creager, Elliot and Jacobsen, Joern-Henrik and Zemel, Richard},
     month = jul,
     year = {2021},
     note = {ISSN: 2640-3498},
     pages = {2189--2200},
   }

2. NeurIPS

   Counterfactual Data Augmentation using Locally Factored Dynamics

   Silviu Pitis, Elliot Creager, and Animesh Garg

   In Advances in Neural Information Processing Systems Dec 2020

   Abs arXiv Bib PDF Code

   Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often sparse, and the dynamics at any given time step can often be decomposed into locally independent\vphantom{} causal mechanisms. Such local causal structures can be leveraged to improve the sample efficiency of sequence prediction and off-policy reinforcement learning. We formalize this by introducing local causal models (LCMs), which are induced from a global causal model by conditioning on a subset of the state space. We propose an approach to inferring these structures given an object-oriented state representation, as well as a novel algorithm for Counterfactual Data Augmentation (CoDA). CoDA uses local structures and an experience replay to generate counterfactual experiences that are causally valid in the global model. We find that CoDA significantly improves the performance of RL agents in locally factored tasks, including the batch-constrained and goal-conditioned settings. Code available at https://github.com/spitis/mrl.

   @inproceedings{pitis_counterfactual_2020,
     title = {Counterfactual {Data} {Augmentation} using {Locally} {Factored} {Dynamics}},
     volume = {33},
     urldate = {2022-09-07},
     booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
     publisher = {Curran Associates, Inc.},
     author = {Pitis, Silviu and Creager, Elliot and Garg, Animesh},
     year = {2020},
     month = dec,
     pages = {3976--3990},
   }

3. ICML

   Flexibly Fair Representation Learning by Disentanglement

   Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel

   In Proceedings of the 36th International Conference on Machine Learning Jun 2019

   Abs arXiv Bib PDF

   We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruction and prediction, but are also flexibly fair, meaning they can be easily modified at test time to achieve subgroup demographic parity with respect to multiple sensitive attributes and their conjunctions. We show empirically that the resulting encoder—which does not require the sensitive attributes for inference—allows for the adaptation of a single representation to a variety of fair classification tasks with new target labels and subgroup definitions.

   @inproceedings{creager_flexibly_2019,
     title = {Flexibly {Fair} {Representation} {Learning} by {Disentanglement}},
     language = {en},
     urldate = {2022-09-07},
     booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}},
     publisher = {PMLR},
     author = {Creager, Elliot and Madras, David and Jacobsen, Joern-Henrik and Weis, Marissa and Swersky, Kevin and Pitassi, Toniann and Zemel, Richard},
     month = jun,
     year = {2019},
     note = {ISSN: 2640-3498},
     pages = {1436--1445},
   }