Cycles in Causal Learning. - Katie Everett; Ian Fischer (CoRR, 2020)

Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0). - Shailesh Bavadekar; Andrew M. Dai; John Davis; Damien Desfontaines; Ilya Eckstein; Katie Everett; Alex Fabrikant; Gerardo Flores; Evgeniy Gabrilovich; Krishna Gadepalli; Shane Glass; Rayman Huang; et al. (CoRR, 2020)

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. - Sébastien Lachapelle; Pau Rodríguez; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CLeaR, 2022)

GFlowNets and variational inference. - Nikolay Malkin; Salem Lahlou; Tristan Deleu; Xu Ji; Edward J. Hu; Katie Everett; Dinghuai Zhang; Yoshua Bengio (ICLR, 2023)

GFlowNet-EM for Learning Compositional Latent Variable Models. - Edward J. Hu; Nikolay Malkin; Moksh Jain; Katie E. Everett; Alexandros Graikos; Yoshua Bengio (ICML, 2023)

Small-scale proxies for large-scale Transformer training instabilities. - Mitchell Wortsman; Peter J. Liu; Lechao Xiao; Katie E. Everett; Alexander A. Alemi; Ben Adlam; John D. Co-Reyes; Izzeddin Gur; Abhishek Kumar; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein; et al. (ICLR, 2024)

Scaling Exponents Across Parameterizations and Optimizers. - Katie E. Everett; Lechao Xiao; Mitchell Wortsman; Alexander A. Alemi; Roman Novak; Peter J. Liu; Izzeddin Gur; Jascha Sohl-Dickstein; Leslie Pack Kaelbling; Jaehoon Lee 001; Jeffrey Pennington (ICML, 2024)

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. - Sébastien Lachapelle; Pau Rodríguez López; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CoRR, 2024)

Dimension-adapted Momentum Outscales SGD. - Damien Ferbach; Katie Everett; Gauthier Gidel; Elliot Paquette; Courtney Paquette (CoRR, 2025)