Conference Information

DSML 2018: Dependable and Secure Machine Learning

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Submission Date:
2018-04-01
Notification Date:
2018-05-01
Conference Date:
2018-06-25
Location:
Luxembourg City, Luxembourg
Viewed: 10943   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

42.1 / 100
Ranked #4,037 of 5,646 conferences · Top 72%

#256 of 357 in Security & Privacy #269 of 341 in Systems & Architecture #509 of 733 in Artificial Intelligence & Machine Learning

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-08-30

Call For Papers

DSML 2018 (Dependable and Secure Machine Learning) is an academic conference held in Luxembourg City, Luxembourg on 2018-06-25. The paper submission deadline is 2018-04-01. Acceptance notifications are sent on 2018-05-01.

Machine learning (ML) is increasingly used in critical domains such as health and wellness, criminal sentencing recommendations, commerce, transportation, human capital management, entertainment, and communication. The design of ML systems has mainly focused on developing models, algorithms, and datasets on which they are trained to demonstrate high accuracy for specific tasks such as object recognition and classification. Machine learning algorithms typically construct a model by training on a labeled training dataset and their performance is assessed based on the accuracy in predicting labels for unseen (but often similar) testing data. This is based on the assumption that the training dataset is representative of the inputs that the system will face in deployment. However, in practice there are a wide variety of unexpected accidental, as well as adversarially-crafted, perturbations on the ML inputs that might lead to violations of this assumption. Further, ML algorithms are often executed on special-purpose hardware accelerators, which may themselves be subject to faults. Thus, there is a growing concern regarding the reliability, safety, security, and accountability of machine learning systems. 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 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
Last updated by Dou Sun on

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