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Katie Everett
Katie Everett
Google DeepMind, MIT
E-mail confirmado em google.com
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
S Lachapelle, P Rodriguez, Y Sharma, KE Everett, R Le Priol, A Lacoste, ...
Conference on Causal Learning and Reasoning, 428-484, 2022
1442022
GFlowNets and variational inference
N Malkin, S Lahlou, T Deleu, X Ji, E Hu, K Everett, D Zhang, Y Bengio
arXiv preprint arXiv:2210.00580, 2022
902022
Small-scale proxies for large-scale transformer training instabilities
M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ...
arXiv preprint arXiv:2309.14322, 2023
792023
GFlowNet-EM for learning compositional latent variable models
EJ Hu, N Malkin, M Jain, KE Everett, A Graikos, Y Bengio
International Conference on Machine Learning, 13528-13549, 2023
482023
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description
A Kumok, A Fabrikant, AM Dai, C Kamath, C Stanton, D Desfontaines, ...
Technical Report. N/A. URL: https://arxiv. org/abs, 2020
24*2020
Nonparametric partial disentanglement via mechanism sparsity: Sparse actions, interventions and sparse temporal dependencies
S Lachapelle, PR López, Y Sharma, K Everett, RL Priol, A Lacoste, ...
arXiv preprint arXiv:2401.04890, 2024
232024
Scaling exponents across parameterizations and optimizers
K Everett, L Xiao, M Wortsman, AA Alemi, R Novak, PJ Liu, I Gur, ...
arXiv preprint arXiv:2407.05872, 2024
202024
Nanodo: A minimal transformer decoder-only language model implementation in JAX
PJ Liu, R Novak, J Lee, M Wortsman, L Xiao, K Everett, AA Alemi, ...
http://github. com/google-deepmind/nanodo, 2024
8*2024
Cycles in Causal Learning
K Everett, I Fischer
ICLR Workshop on Causal Learning for Decision Making, 2020
2020
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