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Furong Huang
- > Home > Persons > Furong Huang
Publications
- 2024
- [i89]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
Benchmarking the Robustness of Image Watermarks. CoRR abs/2401.08573 (2024) - [i85]Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang:
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. CoRR abs/2402.06659 (2024) - 2023
- [c45]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. ICLR 2023 - [c40]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. NeurIPS 2023 - [i75]Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang:
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. CoRR abs/2302.03015 (2023) - 2022
- [c30]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. NeurIPS 2022 - [c28]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. NeurIPS 2022 - [i52]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR abs/2202.05826 (2022) - [i46]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR abs/2208.09392 (2022) - 2021
- [c24]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. AAAI 2021: 10815-10823 - [c20]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS 2021: 6695-6706 - [c19]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [c18]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. ECML/PKDD (3) 2021: 628-643 - [i39]Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein:
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. CoRR abs/2103.02079 (2021) - [i37]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. CoRR abs/2106.04537 (2021) - [i33]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR abs/2108.01335 (2021) - [i32]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
Datasets for Studying Generalization from Easy to Hard Examples. CoRR abs/2108.06011 (2021) - [i29]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - 2020
- [c15]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - [i24]Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein:
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. CoRR abs/2002.09766 (2020) - [i21]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
Adaptive Learning Rates with Maximum Variation Averaging. CoRR abs/2006.11918 (2020) - [i19]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. CoRR abs/2010.12989 (2020) - 2019
- [c9]Ali Shafahi, Amin Ghiasi, Mahyar Najibi, Furong Huang, John P. Dickerson, Tom Goldstein:
Batch-wise Logit-Similarity: Generalizing Logit-Squeezing and Label-Smoothing. BMVC 2019: 72 - [i16]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization through Visualizations. CoRR abs/1906.03291 (2019) - [i15]Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein:
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training? CoRR abs/1910.11585 (2019) - 2016
- [i7]Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein:
Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR abs/1612.03350 (2016)
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