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Workshop
Machine Learning Challenges as a Research Tool
Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy

Sat Dec 09 08:00 AM -- 06:30 PM (PST) @ S1
Event URL: http://ciml.chalearn.org/ciml2017 »

Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. The playful nature of challenges naturally attracts students, making challenge a great teaching resource. For this fourth edition of the CiML workshop at NIPS we want to explore the impact of machine learning challenges as a research tool. The workshop will give a large part to discussions around several axes: (1) benefits and limitations of challenges as a research tool; (2) methods to induce and train young researchers; (3) experimental design to foster contributions that will push the state of the art.
CiML is a forum that brings together workshop organizers, platform providers, and participants to discuss best practices in challenge organization and new methods and application opportunities to design high impact challenges. Following the success of last year's workshop, in which a fruitful exchange led to many innovations, we propose to reconvene and discuss new opportunities for challenges as a research tool, one of the hottest topics identified in last year's discussions. We have invited prominent speakers in this field.
We will also reserve time to an open discussion to dig into other topic including open innovation, collaborative challenges (coopetitions), platform interoperability, and tool mutualisation.
The audience of this workshop is targeted to workshop organizers, participants, and anyone with scientific problem involving machine learning, which may be formulated as a challenge. The emphasis of the workshop is on challenge design. Hence it complements nicely the workshop on the NIPS 2017 competition track and will help paving the way toward next year's competition program.

Author Information

Isabelle Guyon (U. Paris-Saclay & ChaLearn)

Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.

Evelyne Viegas (Microsoft Research)
Sergio Escalera (University of Barcelona and Computer Vision Center)

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 Campus UAB. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is advisor, director, and 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, and he is vice-chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 150 research papers and participated in the organization of scientific events, including CCIA04, CCIA14, ICCV11, AMDO2016, FG2017, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16. He has been guest editor at JMLR, TPAMI, IJCV, TAC, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, and FG17. His research interests include, between others, statistical pattern recognition, visual object recognition, and HCI systems, with special interest in human pose recovery and behavior analysis from multi-modal data.

Jacob D Abernethy (University of Michigan)

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