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Weijie J Su

Showing results (1-10 of 15) with videos related to

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Proceedings of the National Academy of Sciences of the United States of America|August 28, 2023
A law of data separation in deep learningHangfeng He, Weijie J Su
Physical Review. E|October 21, 2025
A law of next-token prediction in large language modelsHangfeng He, Weijie J Su
Scientific Reports|March 28, 2025
Tackling copyright issues in AI image generation through originality estimation and genericizationHiroaki Chiba-Okabe, Weijie J Su
Proceedings of the National Academy of Sciences of the United States of America|November 5, 2025
The 2020 US Decennial Census is more private than you (might) thinkBuxin Su, Weijie J Su, Chendi Wang
Harvard Data Science Review|November 30, 2020
Deep Learning with Gaussian Differential PrivacyZhiqi Bu, Jinshuo Dong, Qi Long, et al.
Journal of Machine Learning Research : JMLR|August 6, 2024
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?Shuxiao Chen, Qinqing Zheng, Qi Long, et al.
Proceedings of Machine Learning Research|July 22, 2021
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth ExpansionQinqing Zheng, Jinshuo Dong, Qi Long, et al.
Proceedings of Machine Learning Research|August 5, 2021
Federated <i>f</i>-Differential PrivacyQinqing Zheng, Shuxiao Chen, Qi Long, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 22, 2021
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced trainingCong Fang, Hangfeng He, Qi Long, et al.
Proceedings of Machine Learning Research|August 29, 2024
Bridging the Gap: Rademacher Complexity in Robust and Standard GeneralizationJiancong Xiao, Ruoyu Sun, Qi Long, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Proceedings of the National Academy of Sciences of the United States of America|August 28, 2023
A law of data separation in deep learningHangfeng He, Weijie J Su
Physical Review. E|October 21, 2025
A law of next-token prediction in large language modelsHangfeng He, Weijie J Su
Scientific Reports|March 28, 2025
Tackling copyright issues in AI image generation through originality estimation and genericizationHiroaki Chiba-Okabe, Weijie J Su
Proceedings of the National Academy of Sciences of the United States of America|November 5, 2025
The 2020 US Decennial Census is more private than you (might) thinkBuxin Su, Weijie J Su, Chendi Wang
Harvard Data Science Review|November 30, 2020
Deep Learning with Gaussian Differential PrivacyZhiqi Bu, Jinshuo Dong, Qi Long, et al.
Journal of Machine Learning Research : JMLR|August 6, 2024
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?Shuxiao Chen, Qinqing Zheng, Qi Long, et al.
Proceedings of Machine Learning Research|July 22, 2021
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth ExpansionQinqing Zheng, Jinshuo Dong, Qi Long, et al.
Proceedings of Machine Learning Research|August 5, 2021
Federated <i>f</i>-Differential PrivacyQinqing Zheng, Shuxiao Chen, Qi Long, et al.
Proceedings of the National Academy of Sciences of the United States of America|October 22, 2021
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced trainingCong Fang, Hangfeng He, Qi Long, et al.
Proceedings of Machine Learning Research|August 29, 2024
Bridging the Gap: Rademacher Complexity in Robust and Standard GeneralizationJiancong Xiao, Ruoyu Sun, Qi Long, et al.
Pageof 2