organizers
Challenges in Machine Learning:
Machine Learning Challenges as a Research Tool
Saturday December 9, 2017, Long Beach, California
NIPS 2017 workshops. Long Beach Convention Center
[Home][Schedule][Invited Speakers][Committee]
For any question, please contact us by email at nips2017@chalearn.org.
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 Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. Besides Machine Learning, his research interest is discovering connections between Optimization, Statistics, and Economics. He graduated from UC Berkeley (Peter Bartlett advisor) in 2011, and was a Simons postdoctoral fellow with Michael Kearns for the following two years.
With Percy Liang, he created MLCOMP http://mlcomp.org/help/about_us.html, a platform to benchmark machine learning algorithms. He is currently involved in organizing challenges in machine learning with students as the University of Michigan.
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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 a member of the Visual and Computational Learning consolidated research group of Catalonia. 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 Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-founder of PhysicalTech and Care Respite companies. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, INNS, and Chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 200 research papers and participated in the organization of scientific events, including CCIA04, CCIA14, ICCV11, AMDO16, FG17, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16, CVPR17. He has been guest editor at JMLR, TPAMI, IJCV, TAC, PR, JIVP, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, AVSS17, FG17, and ICCV17, and competition and demo chair at FG17 and NIPS17. His research interests include, between others, statistical pattern recognition, affective computing, and human pose recovery and behavior understanding, including multi-modal data analysis.
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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 upcoming 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.