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Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization


Jun 11, 2021
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish


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Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?


Jun 05, 2021
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

* Accepted to ICML2021 as long talk 

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Neural Approximate Sufficient Statistics for Implicit Models


Oct 20, 2020
Yanzhi Chen, Dinghuai Zhang, Michael Gutmann, Aaron Courville, Zhanxing Zhu


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Informative Dropout for Robust Representation Learning: A Shape-bias Perspective


Aug 10, 2020
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

* Accepted to ICML2020. Code is available at https://github.com/bfshi/InfoDrop 

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Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework


Feb 21, 2020
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu


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You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle


May 23, 2019
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong


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