Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)

Pin Yu Chen; Cho Jui Hsieh; Bo Li; Sijia Liu

doi:10.1145/3447548.3469455

Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)

Pin Yu Chen, Cho Jui Hsieh, Bo Li, Sijia Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'21 workshop consists of three tracks: (i) open-call paper submissions; (ii) invited speakers; and (iii) rising star awards and presentations. The full details about the workshop can be found at https://sites.google.com/view/advml.

Original language	English (US)
Title of host publication	KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4112-4113
Number of pages	2
ISBN (Electronic)	9781450383325
DOIs	https://doi.org/10.1145/3447548.3469455
State	Published - Aug 14 2021
Externally published	Yes
Event	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore Duration: Aug 14 2021 → Aug 18 2021

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/Territory	Singapore
City	Virtual, Online
Period	8/14/21 → 8/18/21

Keywords

adversarial machine learning
adversarial robustness

ASJC Scopus subject areas

Software
Information Systems

Online availability

10.1145/3447548.3469455

Library availability

Discover UIUC Full Text

Cite this

Chen, P. Y., Hsieh, C. J., Li, B., & Liu, S. (2021). Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4112-4113). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3447548.3469455

Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). / Chen, Pin Yu; Hsieh, Cho Jui; Li, Bo et al.

KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. p. 4112-4113 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, PY, Hsieh, CJ, Li, B & Liu, S 2021, Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). in KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 4112-4113, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, Virtual, Online, Singapore, 8/14/21. https://doi.org/10.1145/3447548.3469455

Chen PY, Hsieh CJ, Li B, Liu S. Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2021. p. 4112-4113. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3447548.3469455

Chen, Pin Yu ; Hsieh, Cho Jui ; Li, Bo et al. / Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. pp. 4112-4113 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)",

abstract = "Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'21 workshop consists of three tracks: (i) open-call paper submissions; (ii) invited speakers; and (iii) rising star awards and presentations. The full details about the workshop can be found at https://sites.google.com/view/advml. ",

keywords = "adversarial machine learning, adversarial robustness",

author = "Chen, {Pin Yu} and Hsieh, {Cho Jui} and Bo Li and Sijia Liu",

note = "Funding Information: Pin-Yu Chen is currently a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of on-going MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science and M.A. degree in Statistics from the University of Michigan, Ann Arbor, USA, in 2016. He received his M.S. degree in communication engineering from National Taiwan University, Taiwan, in 2011 and B.S. degree in electrical engineering and computer science (undergraduate honors program) from National Chiao Tung University, Taiwan, in 2009. Dr. Chen{\textquoteright}s recent research is on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. He has published more than 30 papers on trustworthy machine learning at major AI and machine learning conferences, given tutorials at CVPR({\textquoteright}20,{\textquoteright}21), ECCV{\textquoteright}20, ICASSP{\textquoteright}20, KDD{\textquoteright}19 and Big Data{\textquoteright}18, and co-organized several workshops for adversarial machine learning. His research interest also includes graph and network data analytics and their applications to data mining, machine learning, signal processing, and cyber security. He was the recipient of the Chia-Lun Lo Fellowship from the University of Michigan Ann Arbor. He received the NIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award. Cho-Jui Hsieh is an assistant professor in the Computer Science department at University of California, Los Angeles. His research focus is on large-scale optimization and robustness of machine learning models. Cho-Jui obtained his master degree in 2009 from National Taiwan University (advisor: Chih-Jen Lin) and Ph.D. from University of Texas at Austin in 2015 (advisor: Inderjit S. Dhillon). He is the recipient of IBM Ph.D. fellowships in 2013-2015, the best paper award in KDD 2010, ICDM 2012 and ICPP 2018 and best paper finalist in SC 2019. Bo Li is an assistant professor in Computer Science at University of Illinois at Urbana-Champaign. She is the recipient of the Symantec Research Labs Fellowship, Rising Stars, MIT Technology Review TR-35 award, Intel Rising Star award, Amazon Research Award, and best paper awards in several machine learning and security confer-encesHer research focuses on machine learning, security, privacy, game theory, social networks, and adversarial deep learning. She has designed several robust learning algorithms, a scalable frame-work for achieving robustness for a range of learning methods, and privacy preserving data publishing systems. She is interested in both theoretical analysis of general machine learning models and developing practical systems. She is the program committee member NIPS 2016, AAAI 2016, ICML 2016, AAMAS 2016, NDSS 2017. She has Co-organize NIPS workshops 2017, AAAI 2018, and ICLR 2021. Sijia Liu is an Assistant Professor in Computer Science & Engineering at Michigan State University (MSU). He received the Ph.D. degree (with All University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. Prior to joining MSU, he was a Research Staff Member at the MIT-IBM Watson AI Lab, IBM Research, from 2018-2020. He is mainly working on trustworthy and scalable machine learning. In particular, he has strong expertise in adversarial attack and defense, robust learning theory and methods, and fair and explainable machine learning. He has published over 30 papers in top-tier machine learning conferences and journals such as NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, and ECCV. He has given tutorials on zeroth-order optimization for trustworthy ML at Big Data{\textquoteright}18, KDD{\textquoteright}19 and CVPR{\textquoteright}20, and co-organized several workshops on trustworthy and safe AI. He is also the recipient of the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 ; Conference date: 14-08-2021 Through 18-08-2021",

year = "2021",

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doi = "10.1145/3447548.3469455",

language = "English (US)",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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AU - Hsieh, Cho Jui

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AU - Liu, Sijia

N1 - Funding Information: Pin-Yu Chen is currently a research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of on-going MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science and M.A. degree in Statistics from the University of Michigan, Ann Arbor, USA, in 2016. He received his M.S. degree in communication engineering from National Taiwan University, Taiwan, in 2011 and B.S. degree in electrical engineering and computer science (undergraduate honors program) from National Chiao Tung University, Taiwan, in 2009. Dr. Chen’s recent research is on adversarial machine learning and robustness of neural networks. His long-term research vision is building trustworthy machine learning systems. He has published more than 30 papers on trustworthy machine learning at major AI and machine learning conferences, given tutorials at CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19 and Big Data’18, and co-organized several workshops for adversarial machine learning. His research interest also includes graph and network data analytics and their applications to data mining, machine learning, signal processing, and cyber security. He was the recipient of the Chia-Lun Lo Fellowship from the University of Michigan Ann Arbor. He received the NIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper Award. Cho-Jui Hsieh is an assistant professor in the Computer Science department at University of California, Los Angeles. His research focus is on large-scale optimization and robustness of machine learning models. Cho-Jui obtained his master degree in 2009 from National Taiwan University (advisor: Chih-Jen Lin) and Ph.D. from University of Texas at Austin in 2015 (advisor: Inderjit S. Dhillon). He is the recipient of IBM Ph.D. fellowships in 2013-2015, the best paper award in KDD 2010, ICDM 2012 and ICPP 2018 and best paper finalist in SC 2019. Bo Li is an assistant professor in Computer Science at University of Illinois at Urbana-Champaign. She is the recipient of the Symantec Research Labs Fellowship, Rising Stars, MIT Technology Review TR-35 award, Intel Rising Star award, Amazon Research Award, and best paper awards in several machine learning and security confer-encesHer research focuses on machine learning, security, privacy, game theory, social networks, and adversarial deep learning. She has designed several robust learning algorithms, a scalable frame-work for achieving robustness for a range of learning methods, and privacy preserving data publishing systems. She is interested in both theoretical analysis of general machine learning models and developing practical systems. She is the program committee member NIPS 2016, AAAI 2016, ICML 2016, AAMAS 2016, NDSS 2017. She has Co-organize NIPS workshops 2017, AAAI 2018, and ICLR 2021. Sijia Liu is an Assistant Professor in Computer Science & Engineering at Michigan State University (MSU). He received the Ph.D. degree (with All University Doctoral Prize) in Electrical and Computer Engineering from Syracuse University, NY, USA, in 2016. Prior to joining MSU, he was a Research Staff Member at the MIT-IBM Watson AI Lab, IBM Research, from 2018-2020. He is mainly working on trustworthy and scalable machine learning. In particular, he has strong expertise in adversarial attack and defense, robust learning theory and methods, and fair and explainable machine learning. He has published over 30 papers in top-tier machine learning conferences and journals such as NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, and ECCV. He has given tutorials on zeroth-order optimization for trustworthy ML at Big Data’18, KDD’19 and CVPR’20, and co-organized several workshops on trustworthy and safe AI. He is also the recipient of the Best Student Paper Award at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Publisher Copyright: © 2021 Owner/Author.

PY - 2021/8/14

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N2 - Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'21 workshop consists of three tracks: (i) open-call paper submissions; (ii) invited speakers; and (iii) rising star awards and presentations. The full details about the workshop can be found at https://sites.google.com/view/advml.

AB - Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'21 workshop consists of three tracks: (i) open-call paper submissions; (ii) invited speakers; and (iii) rising star awards and presentations. The full details about the workshop can be found at https://sites.google.com/view/advml.

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ER -

Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

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