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Tue 7:00 Towards Understanding the Dynamics of the First-Order Adversaries
Zhun Deng · Hangfeng He · Jiaoyang Huang · Weijie Su
Tue 7:00 Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi · Natalie Frank · Mehryar Mohri
Tue 7:00 Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
Sijia Liu · Songtao Lu · Xiangyi Chen · Yao Feng · Kaidi Xu · Abdullah Al-Dujaili · Mingyi Hong · Una-May O'Reilly
Tue 7:00 Randomized Smoothing of All Shapes and Sizes
Greg Yang · Tony Duan · J. Edward Hu · Hadi Salman · Ilya Razenshteyn · Jerry Li
Tue 7:00 Accelerated Stochastic Gradient-free and Projection-free Methods
Feihu Huang · Lue Tao · Songcan Chen
Tue 8:00 Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Yun Yun Tsai · Pin-Yu Chen · Tsung-Yi Ho
Tue 8:00 Overfitting in adversarially robust deep learning
Leslie Rice · Eric Wong · Zico Kolter
Tue 8:00 Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan · Jinwoo Shin · Sung Ju Hwang
Tue 8:00 Parameterized Rate-Distortion Stochastic Encoder
Quan Hoang · Trung Le · Dinh Phung
Tue 8:00 Stronger and Faster Wasserstein Adversarial Attacks
Kaiwen Wu · Allen Wang · Yaoliang Yu
Tue 9:00 Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
RaphaĆ«l Dang-Nhu · Gagandeep Singh · Pavol Bielik · Martin Vechev
Tue 9:00 Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu · Xiao Zhang · David Evans