This year, I am co-organizing a workshop on anomaly detection for the international conference on machine learning (ICML). So far, we got a really nice lineup of distinguished speakers and are currently trying to setup a useful schedule (which will probably also include some kind of panel discussion). The text below is the call for participation and we hope we get lots of interesting papers …
CALL FOR PAPERS
ICML 2016 Anomaly Detection Workshop
Date: June 24th, 2016
Location: New York, USA
Submission deadline: May 1st, 2016.
Acceptance decisions: May 10th, 2016.
Anomaly, outlier and novelty detection methods are crucial tools in any data scientist’s inventory and are critical components of many real-world applications. Abnormal user activities can be used to detect credit card fraud, network intrusions or other security breaches. In computational biology, characterization of systematic anomalies in gene expression can be translated into clinically relevant information. With the rise of Internet-of-Things, the task of monitoring and diagnostics of numerous autonomous systems becomes intractable for a human and needs to be outsources to a machine. Early detection of an upcoming earthquake or tsunami can potentially save human lives. These applications make anomaly detection methods increasingly relevant in the modern world.
However, with the advent of Big Data, new challenges and questions are introduced, which will need to be addressed by the next generation of the anomaly and outlier detection algorithms. The goal of our workshop is to survey the existing techniques and discuss new research directions in this area:
1. How can one detect anomalies in streaming data?
2. How to address non-stationarity in data when the data distribution (and anomalies) is changing with time?
3. How to detect anomalies as early as possible?
4. How to perform “structured anomaly detection”? I.e., how to detect anomalies that are sequences, trees, graphs, or in general, data that violates the IID assumptions?
5. Can weakly labeled or partially labeled data help? If so, then how do we take advantage of this labeled data?
6. How can we deal with huge data sizes?
7. Can we learn meaningful data representations for anomaly detection?
8. How can we accurately evaluate performance in settings with strongly unbalanced datasets or positive (and unlabeled) examples only?
9. Can we perform “significant anomaly detection” (e.g., using p-values)?
10. What is an “anomaly”? How does it compare to outliers, novelties and the like and can they be tackled with the same methodologies?
11. How can we explain decisions of anomaly detectors to guide human experts?
We solicit submission of research papers in the area of anomaly, outlier and novelty detection with the focus on the topics outlined above. Relevant papers that have been recently published or presented elsewhere are allowed, provided that previous publications are explicitly acknowledged. Submission must adhere to ICML 2016 style format and max. 4 pages long, including figures (+additional fifth page containing cited references, supplementary material can be provided). As the review process is not blind authors may reveal their identity during submission process. Please submit your manuscripts to firstname.lastname@example.org.
All accepted papers will have a poster presentation and the best papers will be selected for an oral presentation.
Leman Akoglu, Stony Brook University
Thomas Dietterich, Oregon State University
Klaus-Robert Mueller, Berlin Institute of Technology
Clayton Scott, University of Michigan
Bernhard Schoelkopf*, Max Planck Institute for Intelligent Systems
Nico Goernitz (Berlin Institute of Technology)
Marius Kloft (Humboldt University of Berlin)
Vitaly Kuznetsov (Courant Institute)