Advances in Modeling and Learning Interactions from Complex Data

December 8, 2017

Long Beach, CA

Submission deadline extended!!

Submission deadline : Oct. 27, 2017 Nov 3, 2017

Author notification: Nov. 10, 2017 Nov 17, 2017


Whether it is biological networks of proteins and genes or technological ones like sensor networks and the Internet, we are surrounded today by complex systems composed of entities interacting with and affecting each other. An urgent need has therefore emerged for developing novel techniques for modeling, learning, and conducting inference in such networked systems. Consequently, we have seen progress from a variety of disciplines in both fundamental methodology and in applications of such methods to practical problems. However, much work remains to be done, and a unifying and principled framework for dealing with these problems remains elusive.

This workshop aims to bring together theoreticians and practitioners in order to both chart out recent advances and to discuss new directions in understanding interactions in large and complex systems. NIPS, with its attendance by a broad and cross-disciplinary set of researchers offers the ideal venue for this exchange of ideas.

The workshop will feature a mix of contributed talks, contributed posters, and invited talks by leading researchers from diverse backgrounds working in these areas. We will also have a specific segment of the schedule reserved for the presentation of open problems, and will have plenty of time for discussions where we will explicitly look to spark off collaborations amongst the attendees.

We encourage submissions in a variety of topics including, but not limited to:

  • Computationally and statistically efficient techniques for learning graphical models from data including convex, greedy, and adaptive approaches.
  • New probabilistic models of interacting systems including nonparametric and exponential family graphical models.
  • Community detection algorithms including semi-supervised and adaptive approaches.
  • Techniques for modeling and learning causal relationships from data.
  • Bayesian techniques for modeling complex data and causal relationships.
  • Kernel methods for directed and undirected graphical models.
  • Applications of these methods in various areas like sensor networks, computer networks, social networks, and phylogenetic trees and graphs.

Successful submissions will emphasize the role of statistical and computational learning to the problem at hand. The author(s) of these submissions will be invited to present their work as either a poster or as a contributed talk.


Alongside the above, we also solicit submissions of open problems that go with the theme of the workshop. The authors of the selected open problems will be able to present the problem to the attendees and solicit feedback/collaborations.


Submission deadline (extended!): Oct. 27, 2017 Nov 3, 2017

Author notification: Nov. 10, 2017 Nov 17, 2017


Both papers and open problems submitted to the workshop should be no longer than four pages (including references). They should be prepared as a camera-ready manuscript using the NIPS style.

Please upload submissions (.pdf, upto 5mb) at the CMT website. Accepted submissions will be presented as talks or posters.


Participants should refer to the NIPS 2017 website for information on how to register for the workshop.


For questions or comments, please contact us at: