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DSML 2018 : International Workshop on Dependable and Secure Machine Learning (DSML)

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Link: https://dependablesecureml.github.io
 
When Jun 25, 2018 - Jun 25, 2018
Where Luxembourg City, Luxembourg
Submission Deadline Apr 1, 2018
Notification Due May 1, 2018
 

Call For Papers

First International Workshop on Dependable and Secure Machine Learning (DSML)
Co-located with the 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2018)
25 June 2018, Luxembourg City, Luxembourg
https://dependablesecureml.github.io

Call for Papers:
The DSN Workshop on Dependable and Secure Machine Learning (DSML) is an open forum for researchers, practitioners, and regulatory experts, to present and discuss innovative ideas and practical techniques and tools for producing dependable and secure machine learning (ML) systems. A major goal of the workshop is to draw the attention of the research community to the problem of establishing guarantees of reliability, security, safety, and robustness for systems that incorporate increasingly complex ML models, and to the challenge of determining whether such systems can comply with requirements for safety-critical systems. A further goal is to build a research community at the intersection of machine learning and dependable and secure computing.

Topics of Interest:
- Testing, certification, and verification of ML models and algorithms
- Metrics for benchmarking the robustness of ML systems
- Adversarial machine learning (attacks and defenses)
- Resilient and repairable ML models and algorithms
- Reliability and security of ML architectures, computing platforms, and distributed systems
- Faults in implementation of ML algorithms and their consequences
- Dependability of ML accelerators and hardware platforms
- Safety and societal impact of machine learning

Important dates (AoE):
- Submission Deadline: 1 April, 2018
- Notification of Acceptance: 1 May, 2018
- Workshop: 25 June 2018

Submissions:
DSML welcomes both research papers reporting results from mature work, and more speculative papers describing new ideas with preliminary exploratory work. Papers reporting industry experiences and case studies are also encouraged. We solicit submissions in the following formats:
- Regular research papers (up to 6 pages)
- Position or experience papers (up to 3 pages)

All submissions should be in PDF format and must adhere to the IEEE Computer Society 8.5″x11″ two-column camera-ready format (using a 10-point font on 12-point single-spaced leading). Both LaTeX and MS Word templates are available here:
https://www.ieee.org/conferences_events/conferences/publishing/templates.html

All submitted manuscripts will be peer-reviewed by the program committee. Papers will be accepted and included in the workshop program according to the following criteria: relevance of the addressed topic to the scope of the workshop; novelty and value of the proposed contribution; scientific merit; quality of the writing, presentation accuracy, and style.
Submission URL: https://dsn-dsml18.hotcrp.com

Proceedings:
Authors can select either of the following options for the publication of their accepted papers:
(1) Paper will appear in the supplementary DSN proceedings (archived in the IEEE Digital library),
(2) Paper will not be included in the supplementary DSN proceedings, but the authors are required to post a version of the paper on arxiv that will be linked from the workshop website.

Organizing Committee:
Homa Alemzadeh, University of Virginia
Karthik Pattabiraman, University of British Columbia
David Evans, University of Virginia

Program Committee:
Kamalika Chaudhuri, University of California, San Diego
Shalini Ghosh, Stanford Research Institute (SRI)
Zbigniew Kalbarczyk, University of Illinois (UIUC)
Dong Seong Kim, University of Canterbury
Philip Koopman, Carnegie Mellon University (CMU)
Aleksander Mądry, Massachusetts Institute of Technology (MIT)
Cristina Nita-Rotaru, Northeastern University
Alina Oprea, Northeastern University
Nicolas Papernot, Penn State University
Gilles Tredan, LAAS-CNRS
Timothy Tsai, Nvidia
Kush Varshney, IBM Research

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