Everett, Katie E

5 publications

ICML 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

ICLR 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

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

ICLR 2023 GFlowNets and Variational Inference Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J Hu, Katie E Everett, Dinghuai Zhang, Yoshua Bengio

CLeaR 2022 Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA Sebastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien