Elliot Creager | DeepAI

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∙ 04/13/2023

SURFSUP: Learning Fluid Simulation for Novel Surfaces

Modeling the mechanics of fluid in complex scenes is vital to applicatio...

0 Arjun Mani, et al. ∙

∙ 10/20/2022

MoCoDA: Model-based Counterfactual Data Augmentation

The number of states in a dynamic process is exponential in the number o...

0 Silviu Pitis, et al. ∙

∙ 11/12/2020

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

Robustness is of central importance in machine learning and has given ri...

58 Robert Adragna, et al. ∙

∙ 10/14/2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization

Standard learning approaches are designed to perform well on average for...

0 Elliot Creager, et al. ∙

∙ 07/31/2020

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

Most recommender systems (RS) research assumes that a user's utility can...

4 Martin Mladenov, et al. ∙

∙ 07/06/2020

Counterfactual Data Augmentation using Locally Factored Dynamics

Many dynamic processes, including common scenarios in robotic control an...

0 Silviu Pitis, et al. ∙

∙ 06/14/2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open...

14 Frederik Träuble, et al. ∙

∙ 09/18/2019

Causal Modeling for Fairness in Dynamical Systems

In this work, we present causal directed acyclic graphs (DAGs) as a unif...

12 Elliot Creager, et al. ∙

∙ 06/06/2019

Flexibly Fair Representation Learning by Disentanglement

We consider the problem of learning representations that achieve group a...

4 Elliot Creager, et al. ∙

∙ 09/07/2018

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data

How do we learn from biased data? Historical datasets often reflect hist...

4 David Madras, et al. ∙

∙ 07/20/2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling

Explanations of black-box classifiers often rely on saliency maps, which...

0 Chun-Hao Chang, et al. ∙

∙ 02/17/2018

Learning Adversarially Fair and Transferable Representations

In this work, we advocate for representation learning as the key to miti...

0 David Madras, et al. ∙

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