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Affinity Workshop
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Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Hanjie Chen · Yangfeng Ji
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Workshop
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Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
Konstantinos Panousis · Sotirios Chatzis · Sergios Theodoridis
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Workshop
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Mon 12:00
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Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Anne Harrington · Arturo Deza
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Datasets and Benchmarks
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Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Qinkai Zheng · Xu Zou · Yuxiao Dong · Yukuo Cen · Da Yin · Jiarong Xu · Yang Yang · Jie Tang
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Workshop
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Efficiently Improving the Robustness of RL Agents against Strongest Adversaries
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang
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Workshop
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Mon 9:55
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Efficiently Improving the Robustness of RL Agents against Strongest Adversaries
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang
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Workshop
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Mon 12:00
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Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN
Jonathan Gant · Andrzej Banburski · Arturo Deza
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Spotlight
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Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Hassan Dbouk · Naresh Shanbhag
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Workshop
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Adversarial Robustness of Program Synthesis Models
Mrinal Anand · Mayank Singh
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Workshop
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Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations
Marek Galovic · Branislav Bosansky · Viliam Lisy
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Workshop
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Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
Xiaoyang Wang · Han Zhao · Klara Nahrstedt · Sanmi Koyejo
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Workshop
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Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations
Marek Galovic · Branislav Bosansky · Viliam Lisy
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