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Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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B

Workshop Agenda

DAY 1: DECEMBER 11, 2018

Session 1: Plenary

8:00 AM Sponsor Remarks and Expectations of the Workshop
David M. Isaacson, Office of the Director of National Intelligence
8:15 Generation of Capability Technology Matrix Tables
Rama Chellappa, Planning Committee Chair, University of Maryland, College Park
George Coyle, Study Director, Intelligence Community Studies Board, National Academies of Sciences, Engineering, and Medicine
8:30 Recent Advances in Machine Learning
Michael Jordan, University of California, Berkeley
9:30 Machine Learning on Perception: Hype vs. Hope
Ruzena Bajcsy, University of California, Berkeley
10:30 Break

Session 2: Adversarial Attacks

11:00 Media Forensics
Matthew Turek, Defense Advanced Research Projects Agency
11:45 Forensic Techniques
Hany Farid, Dartmouth College
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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12:30 PM Lunch

Session 3: Detection and Mitigation of Adversarial Attacks and Anomalies

1:30 Joysula Rao, IBM Corporation
2:15 Circumventing Defenses to Adversarial Examples
Anish Athalye, Massachusetts Institute of Technology
3:00 Break

Session 4: Enablers of Machine Learning Algorithms and Systems

3:30 Impact of Neuroscience on Data Science for Perception
John Tsotsos, York University, Canada
5:30 Capability Technology Matrix Tables Preparation
6:00 Adjourn for the Day

DAY 2: DECEMBER 12, 2018

8:00 AM Sponsor Remarks
David M. Isaacson, Office of the Director of National Intelligence

Session 5: Recent Trends in Machine Learning—1

8:15 On Open Set and Adversarial Issues in Machine Learning
Terry Boult, University of Colorado, Colorado Springs
9:00 GANs for Domain Adaptation and Security Against Attacks
Rama Chellappa, University of Maryland, College Park
9:45 Break

Session 6: Recent Trends in Machine Learning—2

10:00 Recent Advances in Optimization for Machine Learning
Tom Goldstein, University of Maryland
10:45 Forecasting Using Machine Learning
Aram Galstyan, Information Sciences Institute, University of Southern California
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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Session 7: Plenary Session

11:30 Plenary Talk
Dawn Song, University of California, Berkeley
12:30 PM Lunch

Session 8: Recent Trends in Machine Learning—3

1:30 Domain Adaptation
Judy Hoffman, Georgia Institute of Technology
2:15 Explainable Machine Learning
Anna Rohrbach, University of California, Berkeley
3:00 Break

Session 9: Machine Learning System

3:15 Building Domain-Specific Knowledge with Human-in-the-Loop
Yunyao Li, IBM Corporation
4:00 Robust Design of Machine Learning Systems
Anthony Hoogs, Kitware, Inc.

Session 10: Capability Technology Matrix Tables

4:45 Discussion on Preparing the Capability Technology Matrix Tables
5:30 Adjourn Workshop
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page 62
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page 63
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page 64
Next: Appendix C: Workshop Statement of Task »
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The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11–12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

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