Nonnegative Tensor Factorization with Frequency Modulation Cues for Blind Audio Source Separation. - Elliot Creager; Noah D. Stein; Roland Badeau; Philippe Depalle (ISMIR, 2016)

Gradient-based Optimization of Neural Network Architecture. - Will Grathwohl; Elliot Creager; Seyed Kamyar Seyed Ghasemipour; Richard S. Zemel (ICLR (Workshop), 2018)

Learning Adversarially Fair and Transferable Representations. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (ICML, 2018)

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. - Chun-Hao Chang; Elliot Creager; Anna Goldenberg; David Duvenaud (CoRR, 2018)

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (CoRR, 2018)

Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (FAT, 2019)

Explaining Image Classifiers by Counterfactual Generation. - Chun-Hao Chang; Elliot Creager; Anna Goldenberg; David Duvenaud (ICLR (Poster), 2019)

Flexibly Fair Representation Learning by Disentanglement. - Elliot Creager; David Madras; Jörn-Henrik Jacobsen; Marissa A. Weis; Kevin Swersky; Toniann Pitassi; Richard S. Zemel (ICML, 2019)

Causal Modeling for Fairness In Dynamical Systems. - Elliot Creager; David Madras; Toniann Pitassi; Richard S. Zemel (ICML, 2020)

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. - Martin Mladenov; Elliot Creager; Omer Ben-Porat; Kevin Swersky; Richard S. Zemel; Craig Boutilier (ICML, 2020)

Counterfactual Data Augmentation using Locally Factored Dynamics. - Silviu Pitis; Elliot Creager; Animesh Garg (NeurIPS, 2020)

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Anirudh Goyal; Francesco Locatello; Bernhard Schölkopf; Stefan Bauer (CoRR, 2020)

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. - Elliot Creager; Jörn-Henrik Jacobsen; Richard S. Zemel (CoRR, 2020)

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. - Robert Adragna; Elliot Creager; David Madras; Richard S. Zemel (CoRR, 2020)

Environment Inference for Invariant Learning. - Elliot Creager; Jörn-Henrik Jacobsen; Richard S. Zemel (ICML, 2021)

On Disentangled Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Francesco Locatello; Andrea Dittadi; Anirudh Goyal; Bernhard Schölkopf; Stefan Bauer (ICML, 2021)

MoCoDA: Model-based Counterfactual Data Augmentation. - Silviu Pitis; Elliot Creager; Ajay Mandlekar; Animesh Garg (NeurIPS, 2022)

SurfsUp: Learning Fluid Simulation for Novel Surfaces. - Arjun Mani; Ishaan Preetam Chandratreya; Elliot Creager; Carl Vondrick; Richard S. Zemel (ICCV, 2023)

Remembering to Be Fair: On Non-Markovian Fairness in Sequential Decision Making (Preliminary Report). - Parand Alizadeh Alamdari; Toryn Q. Klassen; Elliot Creager; Sheila A. McIlraith (CoRR, 2023)

Robust Machine Learning by Transforming and Augmenting Imperfect Training Data. - Elliot Creager (CoRR, 2023)

Robust Machine Learning by Transforming and Augmenting Imperfect Training Data - Elliot Creager (2023)

Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making. - Parand A. Alamdari; Toryn Q. Klassen; Elliot Creager; Sheila A. McIlraith (ICML, 2024)

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. - Benjamin Eyre; Elliot Creager; David Madras; Vardan Papyan; Richard S. Zemel (ICML, 2024)

Online Algorithmic Recourse by Collective Action. - Elliot Creager; Richard S. Zemel (CoRR, 2024)

Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm. - Rushabh Solanki; Elliot Creager (CoRR, 2024)

Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs. - Brandon Jaipersaud; Zining Zhu 001; Frank Rudzicz; Elliot Creager (CoRR, 2024)

Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Framework. - Cléa Chataigner; Rebecca Ma; Prakhar Ganesh; Afaf Taïk; Elliot Creager; Golnoosh Farnadi (CoRR, 2025)

Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy. - Rushabh Solanki; Meghana Bhange; Ulrich Aïvodji; Elliot Creager (CoRR, 2025)

Transformers Don't In-Context Learn Least Squares Regression. - Joshua Hill; Benjamin Eyre; Elliot Creager (CoRR, 2025)