Accepted Papers:

• Robust Variational Autoencoders: Generating Noise-Free Images from Corrupted Images. Huimin Ren, Yun Yue, Chong Zhou, Randy Paffenroth, Yanhua Li and Matthew Weiss

• Improving the Adversarial Robustness of Transfer Learning via Noisy Feature Distillation. Ting-Wu Chin, Cha Zhang and Diana Marculescu

• PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks. Ting-Wu Chin, Ari Morcos and Diana Marculescu

• On Intrinsic Dataset Properties for Adversarial Machine Learning. Jeffrey Pan and Nicholas Zufelt [MIT-IBM Watson AI Lab best paper award]

• Robustness to large-step adversarial manipulations for a subset of features. Aradhana Sinha, Amer Sinha, Ben Packer, Xuezhi Wang, Nithum Thain, Corey Lane, Ed Chi, Alex Beutel and Jilin Chen [MIT-IBM Watson AI Lab best presentation award]

• Evaluation Indicator for Model Inversion Attack. Hiroaki Tanaka, Wataru Yamada, Keiichi Ochiai, Rina Okada, Satoshi Hasegawa and Daizo Ikeda

• Improving LIME Robustness with Smarter Locality Sampling. Sean Saito, Eugene Chua, Nicholas Capel and Rocco Hu

• Garment Design with Generative Adversarial Networks. Chenxi Yuan and Mohsen Moghaddam

• Identifying Audio Adversarial Examples via Anomalous Pattern Detection. Victor Akinwande, Celia Cintas, Skyler Speakman and Srihari Sridharan

• Is Robust Neurons’ Activation Sufficient to Robustify CNNs against Adversarial Attacks? Jingkang Wang, Gaoyuan Zhang and Sijia Liu

• Deep Partition Aggregation: Provable Defense against General Poisoning Attacks. Alexander Levine and Soheil Feizi [MIT-IBM Watson AI Lab best paper award]

Call for Papers:

Topics of interest include but are not limited to:

• Adversarial attacks (e.g. evasion, poison and model inversion) and defenses in machine learning and data mining

• Robustness certification and property verification techniques

• Representation learning, knowledge discovery and model generalizability

• Model robustness against model compression (e.g. network pruning and quantization)

• Generative models and their applications (e.g., generative adversarial nets)

• Robust optimization methods and (computational) game theory

• Explainable and fair machine learning models via adversarial learning techniques

• Transfer learning, multi-agent adaptation, self-paced learning

• Privacy and security in machine learning systems

• Trustworthy data mining and machine learning