Application Instructions

Eligibility and Requirements: Senior PhD students enrolled in a PhD program before December 2021 or researchers holding postdoctoral positions who obtained PhD degree after April 2022.

Applicants are required to submit the following materials:

• CV (including a list of publications)
• Research statement (up to 2 pages, single column, excluding reference), including your research accomplishments and future research directions
• A 5-minute video recording for your research summary
• Two letters of recommendation uploaded to this form by the referees before September 9th, 2024 (AoE)

The awardee must attend the NeurIPS AdvML-Frontiers workshop and give a presentation in person. Submit the other required materials to this form by September 2nd, 2024 (AoE)

Submission deadline

Application material	Sep 2 '24 AoE	00:00:00
Reference letters	Sep 9 '24 AoE	00:00:00

Submission Instructions

Submission Tracks

We welcome paper submissions from all the following tracks.

Track 1: Regular paper submission. This track accepts papers up to 6 pages with unlimited references or supplementary materials.

Track 2: Blue Sky Ideas/Position paper submission. This track invites submissions up to 6 pages, focusing on the “future” or “current” directions in AdvML. We welcome papers on visionary ideas, long-term challenges, current debates, and overlooked questions. This track aims to serve as an incubator for innovative and provocative research, providing a platform for the exchange of forward-thinking ideas without the constraints of result-oriented standards.

Track 3: Show-and-Tell Demos submission. This track allows papers up to 6 pages to demonstrate the practical innovations done by research and engineering groups. This track aims to create a unique opportunity to showcase the recent developments in the field through tangible demonstrations of systems, applications, services, and solutions.

Please ensure that all submissions conform to the AdvML-Frontiers'24 format template and please submit to OpenReview. Clearly specify the relevant track number in the title of your submission, for instance, by adding \usepackage[track1]{AdvML_Frontiers_2024} at the start of your main LaTeX document. Note that the track number is for review purposes only and will not be included in the final camera-ready version. The accepted papers are non-archival and non-inproceedings. Concurrent submissions are allowed, but it is the responsibility of the authors to verify compliance with other venues' policies. For NeurIPS, any neurips submissions can be submitted concurrently to workshops. Based on the PC’s recommendation, the accepted papers will be allocated either a spotlight talk or a poster presentation.  

Important Dates

Submission deadline	Aug. 30 '24 AoE	00:00:00
Notification to authors	Oct. 9 '24 AoE	00:00:00

Topics

The topics for AdvML-Frontiers'24 include, but are not limited to:

• Adversarial threats on LMMs
• Cross-modal adversarial vulnerabilities for LMMs
• Defensive strategies and adversarial training techniques for LMMs
• Ethical implications of AdvML in LMMs
• Privacy and security in LMMs, (e.g., membership inference attack vs. machine unlearning, watermarking vs. model stealing)
• LMM-aided AdvML (e.g., for attack and defense enhancements)
• Offensive use of LMMs in security
• Novel applications of AdvML for LMMs and LMMs for AdvML
• Mathematical foundations of AdvML (e.g., geometries of learning, causality, information theory)
• Adversarial ML metrics and their interconnections
• New optimization methods for adversarial ML
• Theoretical understanding of adversarial ML
• Data foundations of adversarial ML (e.g., new datasets and new data-driven algorithms)
• Scalable adversarial ML algorithms and implementations
• Adversarial ML in the real world (e.g., physical attacks and lifelong defenses)
• Provably robust machine learning methods and systems
• New adversarial ML applications
• Explainable, transparent, or interpretable ML systems via adversarial learning techniques
• Fairness and bias reduction algorithms in ML
• Adversarial ML for good (e.g., privacy protection, education, healthcare, and scientific discovery)

Contacts

Please contact advml_frontiers24@googlegroups.com for paper submission and logistic questions.

Mathias Humbert (University Lausanne)

Maura Pintor (University of Cagliari)

Maksym Andriuschenko (EPFL)

Yuguang Yao (Michigan State University)

Yiwei Chen (Michigan State University)

Yimeng Zhang (Michigan State University)

Changsheng Wang (Michigan State University)

Soumyadeep Pal (Michigan State University)

Parikshit Ram (IBM Research)

Ruisi Cai (UT Austin (NVIDIA))

Zhenyu Zhang (UT Austin)

Pingzhi Li (UNC)

Haomin Zhuang (Notre Dame)

Changchang Sun (IIT)

Ren Wang (IIT)

Jiabao Ji (UCSB)

Zichen Chen (UCSB)

Deng Pan (University of Notre Dame)

Yao Qiang (Wayne State University)

Rhongho Jang (Wayne State University)

Huaming Chen (The University of Sydney)

Kaiyi Ji (University at Buffalo)

Hossein Hajipour (CISPA)

Siddharth Joshi (UCLA)

Dang Nguyen (UCLA)

Jiayi Ni (UCLA)

Fateme Sheikholeslami (Amazon)

Francesco Croce (EPFL)

Chia-Yi Hsu (NYCU)

Yu-Lin Tsai (NYCU)

Zichong Li (UT Austin)

Zhiyuan He (CUHK)

Shashank Kotyan (Kyushu University)

Zhenhan Huang (RPI)

Wenhan Yang (UCLA)

Jiancheng Liu (Michigan State University)

Chongyu Fan (Michigan State University)

Yuhao Sun (USTC)

Jiaxiang Li (UMN)

Qiucheng Wu (UCSB)

Ioannis Tsaknakis (UMN)

David Pape (CISPA)

Jonathan Evertz (CISPA)

Joel Frank (Ruhr Universität Bochum)

Maximilian Baader (ETH Zurich)

Chirag Agarwal (UVA)

Naman Deep Singh (University of Tubingen)

Christian Schlarmann (University of Tubingen)

Zaitang Li (CUHK)

Chen Xiong (CUHK)

Erh-Chung Chen (NTHU)

Litian Liu (Qualcomm)

Mathilde Raynal (EPFL)

Lena Schoenherr (CISPA)

Yize Li (NEU)

Xin Li (Bosch AI)

Xinlu Zhang (UCSB)

Srishti Gupta (Università di Roma la Sapienza)

Kenan Tang (UCSB)

Prashant Khanduri (Wayne State University)

Ziqi Gao (HKUST)

Aochuan Chen (HKUST)

Junyuan Hong (UT Austin)

Emanuele Ledda (Università di Roma la Sapienza)

Guanhua Zhang (Max Planck Institute for Intelligent Systems)

Giovanni Appruzese (University Liechtenstein)

More to be confirmed ...