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Machine Learning Meets Econometrics (MLECON)
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou

Mon Dec 13 05:00 AM -- 12:10 PM (PST) @ None
Event URL: https://sites.google.com/view/mlecon2021/home »

The Machine Learning Meets Econometrics (MLECON) workshop will serve as an interface for researchers from machine learning and econometrics to understand challenges and recognize opportunities that arise from the synergy between these two disciplines as well as to exchange new ideas that will help propel the fields. Our one-day workshop will consist of invited talks from world-renowned experts, shorter talks from contributed authors, a Gather.Town poster session, and an interdisciplinary panel discussion. To encourage cross-over discussion among those publishing in different venues, the topic of our panel discussion will be “Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation”. It was designed to highlight the complexity of evaluating social and economic programs as well as shortcomings of current approaches in machine learning and opportunities for methodological innovation. These challenges include more complex environments (markets, equilibrium, temporal considerations) and behavior (heterogeneity, delayed effects, unobserved confounders, strategic response). Our team of organizers and program committees is diverse in terms of gender, race, affiliations, country of origin, disciplinary background, and seniority levels. We aim to convene a broad variety of viewpoints on methodological axes (nonparametrics, machine learning, econometrics) as well as areas of application. Our invited speakers and panelists are leading experts in their respective fields and span far beyond the core NeurIPS community. Lastly, we expect participants with diverse backgrounds from various sub-communities of machine learning and econometrics (e.g., non- and semi-parametric econometrics, applied econometrics, reinforcement learning, kernel methods, deep learning, micro- and macro-economics) among other related communities.

Mon 5:00 a.m. - 5:10 a.m.
Welcome and Introduction (Introduction)
Mon 5:10 a.m. - 6:00 a.m.
Poster Session 1 (Poster session)
Mon 6:00 a.m. - 6:30 a.m.
Coffee Break (Break)
Mon 6:30 a.m. - 6:50 a.m.
Invited talk #1 (Invited talk)
Elizabeth A. Stuart
Mon 6:50 a.m. - 7:10 a.m.
Invited talk #2 (Invited talk)
Eric Tchetgen Tchetgen
Mon 7:10 a.m. - 7:20 a.m.
Zoom Q&A for Invited Talk #1 and #2 (Discussion)
Elizabeth A. Stuart, Eric Tchetgen Tchetgen
Mon 7:20 a.m. - 7:35 a.m.
Contributed talks Session 1 (Contributed talk)
Mon 7:35 a.m. - 7:45 a.m.
Zoom Q&A for Contributed talks Session 1 (Discussion)
Mon 7:45 a.m. - 8:30 a.m.
Mon 8:30 a.m. - 8:50 a.m.
Invited talk #3 (Invited talk)
Xiaohong Chen
Mon 8:50 a.m. - 9:10 a.m.
Invited talk #4 (Invited talk)
Vira Semenova
Mon 9:10 a.m. - 9:20 a.m.
Zoom Q&A for Invited Talks #3 and #4 (Discussion)
Xiaohong Chen, Vira Semenova
Mon 9:20 a.m. - 9:40 a.m.
Coffee Break (Break)
Mon 9:40 a.m. - 9:55 a.m.
Contributed talks Session 2 (Contributed talk)
Mon 9:55 a.m. - 10:05 a.m.
Zoom Q&A for Contributed talks Session 2 (Discussion)
Mon 10:05 a.m. - 11:00 a.m.
Panel Discussion: “Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation” (Discussion)
Jennifer Hill, guido imbens, Vasilis Syrgkanis
Mon 11:00 a.m. - 12:00 p.m.
Poster Session 2 (Poster session)
Mon 12:00 p.m. - 12:10 p.m.
Wrapup (Introduction)

Author Information

David Bruns-Smith (UC Berkeley)
Arthur Gretton (Gatsby Unit, UCL)

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).

Limor Gultchin (University of Oxford)
Niki Kilbertus (Helmholtz AI)
Krikamol Muandet (Max Planck Institute for Intelligent Systems)
Evan Munro (Stanford University)
Angela Zhou (Cornell University)

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