Organizers
Challenges in Machine Learning:
Machine Learning challenges "in the wild"
Saturday December 8, 2018
NeurIPS 2018 workshops. Palais des congres de Montreal, Canada
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For any question, please contact us by email at neurips2018@chalearn.org.
Upcoming 2019 volunteer co-chairs: Adrienne Mendrik, Julien Chaumond, Wei-Wei Tu.
Workshop chairs:
Isabelle Guyon (UPSud/INRIA, U. Paris-Saclay and ChaLearn)
Evelyne Viegas (Microsoft Research)
Workshop co-organizers:
Sergio Escalera (U. Barcelona and ChaLearn)
Jacob Abernethy (U. Michigan)
Bios of the organizers:
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Jacob Abernethy is an Assistant Professor in Computer Science at Georgia Tech. He started his faculty career in the Department of Electrical Engineering and Computer Science at the University of Michigan. In October 2011 he finished a PhD in the Division of Computer Science at the University of California at Berkeley, and then spent nearly two years as a Simons postdoctoral fellow at the CIS department at UPenn, working with Michael Kearns. Abernethy's primary interest is in Machine Learning, with a particular focus in sequential decision making, online learning, online algorithms and adversarial learning models. He did his Master's degree at TTI-C, and his Bachelor's Degree at MIT. Abernethy's PhD advisor is Prof. Peter Bartlett.
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Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is also a member of the Computer Vision Center at UAB. He is series editor of The Springer Series on Challenges in Machine Learning. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is Chair of IAPR TC-12: Multimedia and visual information systems. He has published more than 250 research papers and participated in the organization of scientific events, including CCIA04, ICCV11, CCIA14, AMDO16, FG17, NIPS17, NIPS18, FG19, and workshops at ICCV, ICMI, ECCV, CVPR, ICCV, ICPR, NIPS. He has been guest editor at JMLR, TPAMI, IJCV, TAC, PR, MVA, JIVP, Expert Systems, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, AVSS17, FG17, ICCV17, WACV18, FG18, BMVC18, NIPS18, FG19 and competition and demo chair at FG17, NIPS17, NIPS18 and FG19. His research interests include, statistical pattern recognition, affective computing, and human pose recovery and behavior understanding, including multi-modal data analysis, with special interest in characterizing people: personality and psychological profile computing.
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Isabelle Guyon is chaired professor in “big data” at the Université Paris-Saclay, and specializes in statistical data analysis, pattern recognition, machine learning, and causal discovery. Prior to joining Paris-Saclay, she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and coinvented Support Vector Machines (SVM) with Bernhard Boser and Vladimir Vapnik. She is the primary inventor of SVM-RFE, a variable selection technique based on SVM, and co-authored a seminal paper on feature selection that received thousands of citations. Since 2003, she has organized many challenges in machine learning and causal discovery, supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Guyon holds a Ph.D. in Physical Sciences from the University Pierre and Marie Curie, Paris, France. She is president of ChaLearn, a nonprofit dedicated to organizing challenges. She is an action editor of the Journal of Machine Learning Research and general chair of the NIPS 2017 conference.
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Evelyne Viegas is a Director, Artificial Intelligence, at Microsoft. In her current role, she creates initiatives which focus on intelligent information seen as an enabler of innovation, working in partnership with business groups, universities and government agencies worldwide. In particular she develops AI programs which encourage AI experimentation via cloud-based services, and emphasize the notion of coopetitions, or collaborative competitions, to drive open innovation.
Prior to her present role, Evelyne worked as a Technical Lead at Microsoft delivering Natural Language Processing components to Office and Windows. Before Microsoft, and after completing her Ph.D. in France, she worked as a Principal Investigator at the Computing Research Laboratory in New Mexico on an ontology-based Machine Translation project. Evelyne serves on international editorial, program and award committees.