• 09/19/2017
    Call For Papers is out. Deadline for paper submission is October 23. Submit here.
  • 09/15/2017
    Registration for workshops is open through the main conference webpage.

Invited Speakers

Call for Papers

The NIPS17 Workshop on Learning in Presence of Strategic Behavior will be held in conjunction with the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, California, on December 8, 2017.

The main goal of this workshop is to address current challenges and opportunities that arise from the presence of strategic behavior in machine learning. This workshop aims at bringing together members of different communities, including machine learning, economics, theoretical computer science, and social computing, to share recent results, discuss important directions for future research, and foster collaborations.

Papers from a rich set of theoretical and applied perspectives are invited. Some areas of interest at the interface of learning and strategic behavior include, but are not limited to:

  1. Learning from data that is produced by agents who have vested interest in the outcome or the learning process. Examples of this include learning a measure of quality of universities by surveying members of the academia who stand to gain or lose from the outcome, or when a GPS routing app has to learn patterns of traffic delay by routing individuals who have no interest in taking slower routes.
  2. Learning a model for the strategic behavior of one or more agents by observing their interactions. Examples of this include applications of learning in economic paradigms.
  3. Learning as a model of interactions between agents. Examples of this include applications to swarm robotics, where individual agents have to learn to interact in a multi-agent setting in order to achieve individual or collective goals.
  4. Interactions between multiple learners. Examples of this include scenarios where two or more learners learn about the same or multiple related concepts. How do these learners interact? What are the scenarios under which they would share knowledge, information, or data. What are the desirable interactions between learners?

Submissions Instructions

We solicit submission of published and unpublished works. For the former, we request that the authors clearly state the venue of previous publication. Authors are also encouraged to provide a link to an online version of the paper (such as on arXiv). If accepted, such papers will be linked via an index to give an informal record of the workshop. This workshop will have no published proceedings. Accepted submissions will be presented as posters or talks.

Submissions are limited to three pages using the NIPS 2017 format. One additional page containing only cited references is allowed. The review process is not blind. Please use the camera- ready instructions to produce a PDF in the NIPS 2017 format that displays the author’s names.

All submissions should be made through EasyChair on or before October 23, 2017, 11:59pm AoE. Notification of acceptance will be on November 4, 2017. Upon submission on EasyChair, authors will be asked to provide a short summary of their results with the emphasis on their relevance to the theme of the workshop.

Submissions will be evaluated based on their relevance to the theme of the workshop and the novelty of the work.

Important Information

  • Submission Deadline: October 23, 2017, 11:59pm AoE
  • Submission page: EasyChair
  • Notification: November 4, 2017
  • Workshop Date: December 8, 2017

Workshop Registrations

Please refer to the NIPS 2017 website for registration details. We encourage the audience to register for the workshops as soon as possible, as only a limited number of registrations may be available.

Organizing Committee

Please direct your questions and comments regarding the workshop to

Organizing Committee

Jenn Wortman Vaughan

Microsoft Research, New York City

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Nika Haghtalab

Computer Science Department, Carnegie Mellon University

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Yishay Mansour

School of Computer Science, Tel Aviv University

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Tim Roughgarden

Computer Science, Stanford University

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Vasilis Syrgkanis

Microsoft Research, New England

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