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2nd ICML Workshop on New Frontiers in Adversarial Machine Learning
Sijia Liu · Pin-Yu Chen · Dongxiao Zhu · Eric Wong · Kathrin Grosse · Baharan Mirzasoleiman · Sanmi Koyejo

Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Ballroom A
Event URL: https://advml-frontier.github.io/ »

Given the success of AdvML-inspired research, we propose a new edition from our workshop at ICML’22 (AdvML-Frontiers’22), ‘The 2nd Workshop on New Frontiers in AdvML’ (AdvML-Frontiers’23). We target a high-quality international workshop, coupled with new scientific activities, networking opportunities, and enjoyable social events. Scientifically, we aim to identify the challenges and limitations of current AdvML methods and explore new prospective and constructive views for next-generation AdvML across the full theory/algorithm/application stack. As the sequel to AdvML-Frontiers’22, we will continue exploring the new frontiers of AdvML in theoretical understanding, scalable algorithm and system designs, and scientific development that transcends traditional disciplinary boundaries. We will also add new features and programs in 2023. First, we will expand existing research themes, particularly considering the popularity of large foundational models (e.g., DALL-E 2, Stable Diffusion, and ChatGPT). Examples of topics include AdvML for prompt learning, counteracting AI-synthesized fake images and texts, debugging ML from unified data-model perspectives, and ‘green’ AdvML towards environmental sustainability. Second, we will organize a new section, AI Trust in Industry, by inviting industry experts to introduce the practical trend of AdvML, technological innovations, products, and societal impacts (e.g., AI’s responsibility). Third, we will host a Show-and-Tell Demos in the poster session to allow demonstrations of innovations done by research and engineering groups in the industry, academia, and government entities. Fourth, we will collaborate with ‘Black in AI’ (where Co-Organizer Dr. Sanmi Koyejo is serving as the president) to increase the presence and inclusion of Black people in the field of AdvML by creating spaces for sharing ideas and networking.

Author Information

Sijia Liu (Michigan State University & MIT-IBM Watson AI Lab)
Pin-Yu Chen (IBM Research AI)
Dongxiao Zhu (Wayne State University)

Dongxiao Zhu is currently an Associate Professor at Department of Computer Science, Wayne State University. He received the B.S. from Shandong University (1996), the M.S. from Peking University (1999) and the Ph.D. from University of Michigan (2006). Dongxiao Zhu's recent research interests are in Machine Learning and Applications in health informatics, natural language processing, medical imaging and other data science domains. Dr. Zhu is the Director of Machine Learning and Predictive Analytics (MLPA) Lab and the Director of Computer Science Graduate Program at Wayne State University. He has published over 70 peer-reviewed publications and numerous book chapters and he served on several editorial boards of scientific journals. Dr. Zhu's research has been supported by NIH, NSF and private agencies and he has served on multiple NIH and NSF grant review panels. Dr. Zhu has advised numerous students at undergraduate, graduate and postdoctoral levels and his teaching interest lies in programming language, data structures and algorithms, machine learning and data science.

Eric Wong (MIT)
Kathrin Grosse (University of Cagliari)
Baharan Mirzasoleiman (Stanford University)
Sanmi Koyejo (Stanford University)

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