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Yunzhen Feng
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Transferred Discrepancy: Quantifying the Difference Between Representations
Y Feng, R Zhai, D He, L Wang, B Dong
arXiv preprint arXiv:2007.12446, 2020
122020
Enhancing Certified Robustness of Smoothed Classifiers via Weighted Model Ensembling
C Liu, Y Feng, R Wang, B Dong
arXiv preprint arXiv:2005.09363, 2020
92020
Enhancing certified robustness via smoothed weighted ensembling
C Liu, Y Feng, R Wang, B Dong
arXiv preprint arXiv:2005.09363, 2020
72020
A Tale of Tails: Model Collapse as a Change of Scaling Laws
E Dohmatob, Y Feng, P Yang, F Charton, J Kempe
arXiv preprint arXiv:2402.07043, 2024
32024
Embarrassingly Simple Dataset Distillation
Y Feng, SR Vedantam, J Kempe
The Twelfth International Conference on Learning Representations, 2023
22023
Do Efficient Transformers Really Save Computation?
K Yang, J Ackermann, Z He, G Feng, B Zhang, Y Feng, Q Ye, D He, ...
arXiv preprint arXiv:2402.13934, 2024
12024
Model Collapse Demystified: The Case of Regression
E Dohmatob, Y Feng, J Kempe
arXiv preprint arXiv:2402.07712, 2024
12024
Attacking Bayes: Are Bayesian Neural Networks Inherently Robust?
Y Feng, TGJ Rudner, N Tsilivis, J Kempe
Fifth Symposium on Advances in Approximate Bayesian Inference, 2023
2023
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Articles 1–8