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Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

In Neural Information Processing System (NeurIPS), 2022, 2022
Roman Levin and Manli Shu and Eitan Borgnia and Furong Huang and Micah Goldblum and Tom Goldstein
Publisher's website BibTeX

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainabilit

Neural Information Processing System (NeurIPS), 2022.
Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein
Publisher's website BibTeX

Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework

International Conference on Machine Learning (ICML), 2022.
Jiahao Su, Wonmin Byeon, Furong Huang
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Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

The Tenth International Conference on Learning Representations (ICLR), 2022.
Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang
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Transfer RL across Observation Feature Spaces via Model-Based Regularization

The Tenth International Conference on Learning Representations (ICLR), 2022.
Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew E Cohen, Furong Huang
Publisher's website BibTeX

Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL

The Tenth International Conference on Learning Representations (ICLR), 2022.
Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang
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Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency

The Tenth International Conference on Learning Representations (ICLR), 2022.
Xiaoyu Liu, Jiahao Su, Furong Huang
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Understanding the Generalization Benefit of Model Invariance from a Data Perspective

Neural Information Processing System (NeurIPS), 2021.
Sicheng Zhu, Bang An, Furong Huang
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VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Neural Information Processing System (NeurIPS), 2021.
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P Dickerson, Furong Huang, Tom Goldstein
BibTeX

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Neural Information Processing System (NeurIPS), 2021.
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
Publisher's website BibTeX