Provably Learning Diverse Features in Multi-View Data with Midpoint
Mixup

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup

24 October 2022

Muthuraman Chidambaram

Papers citing "Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup"

11 / 11 papers shown

Title
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data Binghui Li Yuanzhi Li OOD 30 2 0 11 Oct 2024
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method Bikang Pan Wei Huang Ye-ling Shi FedML VLM 34 3 0 29 Sep 2024
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation Muthuraman Chidambaram Rong Ge AAML 18 0 0 10 Feb 2024
Pushing Boundaries: Mixup's Influence on Neural Collapse Quinn Fisher Haoming Meng V. Papyan AAML UQCV 38 5 0 09 Feb 2024
Semantic Equivariant Mixup Zongbo Han Tianchi Xie Bing Wu Qinghua Hu Changqing Zhang AAML 59 0 0 12 Aug 2023
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training Binghui Li Yuanzhi Li AAML 26 3 0 02 Jun 2023
Provable Benefit of Mixup for Finding Optimal Decision Boundaries Junsoo Oh Chulee Yun 17 5 0 01 Jun 2023
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim Wonho Choo Hosan Jeong Hyun Oh Song 197 176 0 05 Feb 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 221 1,399 0 04 Dec 2018
Comparison and anti-concentration bounds for maxima of Gaussian random vectors Victor Chernozhukov Denis Chetverikov Kengo Kato 63 219 0 21 Jan 2013