Accepted Papers:

• Huang Dong (University of Hong Kong); Bu Qingwen (The University of Hong Kong); Yuhao Qing (The University of Hong Kong); Haowen Pi (The University of Hong Kong); Heming Cui (The University of Hong Kong). NAGen: Adversarial Training with Neuron-Aware Adversarial Example Generation

• Hari Prasanna Das (UC Berkeley ); Ryan Tran (UC Berkeley); Japjot Singh (UC Berkeley); Yu Wen Lin (UC Berkeley); Costas J. Spanos (University of California at Berkeley). Unsupervised Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training

• Lei Xu (MIT); Laure Berti-Equille (IRD); Alfredo Cuesta Infante (Universidad Rey Juan Carlos); Kalyan Veeramachaneni (MIT). In Situ Augmentation for Defending Against Adversarial Attacks on Text Classifiers [Bon View Publishing Best Paper Award]

• Sandipan Choudhuri (Arizona State University); Hemanth Venkateswara (Arizona State University); Arunabha Sen (Arizona State University). Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation

• Carmelo Ardito (Polytechnic University of Bari); Yashar Deldjoo (Polytechnic University of Bari); Tommaso Di Noia (Polytechnic University of Bari); Eugenio Di Sciascio (Politecnico di Bari); Fatemeh Nazary (Politecnico di Bari); Giovanni Servedio (Polytechnic University of Bari). Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids

• Scott R Oslund (University of California, Santa Cruz); Clayton B Washington (The Ohio State University); Andrew So (California State Polytechnic University at Pomona); Tingting Chen (California State Polytechnic University at Pomona); Hao Ji (California State Polytechnic University at Pomona). Robust Adversarial Stickers for Arbitrary Objects in the Physical World

Call for Papers:

Topics of interest include but are not limited to:

• Adversarial attacks and defenses in machine learning and data mining 

• Provably robust machine learning methods and systems

• Robustness certification and property verification techniques

• Representation learning, knowledge discovery and model generalizability

• 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

• Adversarial machine learning for (social) good

• Novel applications and innovations using adversarial machine learning and data mining