Search Results for author: Chuanlong Xie

Found 8 papers, 0 papers with code

MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

no code implementations NeurIPS 2021 Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li

First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.

Transfer Learning

Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL

no code implementations29 Sep 2021 Siyi Hu, Chuanlong Xie, Xiaodan Liang, Xiaojun Chang

In addition, role diversity can help to find a better training strategy and increase performance in cooperative MARL.

SMAC Starcraft +1

NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models

no code implementations ICCV 2021 Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, Zhenguo Li

Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.

Neural Architecture Search

Towards a Theoretical Framework of Out-of-Distribution Generalization

no code implementations NeurIPS 2021 Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, LiWei Wang

We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.

Domain Generalization Model Selection +1

Provable More Data Hurt in High Dimensional Least Squares Estimator

no code implementations14 Aug 2020 Zeng Li, Chuanlong Xie, Qinwen Wang

Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided.

Risk Variance Penalization

no code implementations13 Jun 2020 Chuanlong Xie, Haotian Ye, Fei Chen, Yue Liu, Rui Sun, Zhenguo Li

The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.

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