Katie Everett

According to our database, Katie Everett authored at least 8 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies.

Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

CoRR, 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, Jeffrey Pennington

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

GFlowNet-EM for Learning Compositional Latent Variable Models.

Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

GFlowNets and variational inference.

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA.

Sébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

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, Chaitanya Kamath, Dennis Kraft, Akim Kumok, Hinali Marfatia, Yael Mayer, Benjamin Miller, Adam Pearce, Irippuge Milinda Perera, Venky Ramachandran, Karthik Raman, Thomas Roessler, Izhak Shafran, Tomer Shekel, Charlotte Stanton, Jacob Stimes, Mimi Sun, Gregory Wellenius, Masrour Zoghi

CoRR, 2020

Cycles in Causal Learning.

Katie Everett, Ian Fischer

CoRR, 2020