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Advances in Modeling and Learning Interactions from Complex Data
Gautam Dasarathy · Mladen Kolar · Richard Baraniuk

Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ 102 A+B
Event URL: https://sites.google.com/view/nips2017interactions/ »

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 active 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 biological networks like 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 these, we also solicit submissions of open problems that go with the theme of the workshop. The author(s) of the selected open problems will be able to present the problem to the attendees and solicit feedback/collaborations.

Author Information

Gautam Dasarathy (Rice University)
Mladen Kolar (University of Chicago)
Richard Baraniuk (Rice University)

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