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Andres Potapczynski
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PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
S Lofti, M Finzi, S Kapoor, A Potapczynski, M Goldblum, AG Wilson
36th Conference on Neural Information Processing Systems, 2022
402022
Simple and Fast Group Robustness by Automatic Feature Reweighting
S Qiu, A Potapczynski, P Izmailov, AG Wilson
arXiv preprint arXiv:2306.11074, 2023
222023
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
A Potapczynski, G Loaiza-Ganem, JP Cunningham
Advances in Neural Information Processing Systems 33, 12311-12321, 2020
192020
Bias-Free Scalable Gaussian Processes via Randomized Truncations
A Potapczynski, L Wu, D Biderman, G Pleiss, JP Cunningham
International Conference on Machine Learning, 8609-8619, 2021
172021
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
M Finzi, A Potapczynski, M Choptuik, AG Wilson
arXiv preprint arXiv:2304.14994, 2023
72023
Low-Precision Arithmetic for Fast Gaussian Processes
W Maddox, A Potapczynski, AG Wilson
38th Conference on Uncertainty in Artificial Intelligence, 2022
72022
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
A Potapczynski, M Finzi, G Pleiss, AG Wilson
arXiv preprint arXiv:2309.03060, 2023
42023
On the Normalizing Constant of the Continuous Categorical Distribution
E Gordon-Rodriguez, G Loaiza-Ganem, A Potapczynski, JP Cunningham
arXiv preprint arXiv:2204.13290, 2022
12022
OMI Research Newsletter–May 2023
M FINZI, A POTAPCZYNSKI, M CHOPTUIK, AG WILSON
Understanding the Generalization of Deep Neural Networks through PAC-Bayes bounds: A Survey
A Potapczynski, S Lotfi, A Chen, C Ick
Supplementary Materials for Low Precision Arithmetic for Fast Gaussian Processes
WJ Maddox, A Potapczynski, AG Wilson
Order 10 (16), 10-12, 0
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