[Colloquium] TTIC Colloquium: Amir Globerson, Hebrew University

[Julia MacGlashan]

*REMINDER*

When:             *Tuesday, Sept 28 @ 11:00am
*

Where:           * TTIC Conference Room #526*, 6045 S Kenwood Ave, 5th Floor


Who:              * **Amir Globerson*, The Hebrew University of Jerusalem


Title:          *      **Learning with Approximate inference - From LP
Relaxations to Pseudo-Max Approaches*****



 Supervised learning problems often involve the prediction of complex
structure labels, such as sequences (e.g., POS tagging) or trees (e.g.,
dependency parsing). To achieve high accuracy in these tasks, one is often
interested in introducing complex dependencies between label parts. However,
this can result in prediction problems that are NP hard. A natural approach
in these cases is to use tractable approximations of the prediction problem.

In this talk I will present our recent work on using approximate inference
for structured prediction tasks. I will describe linear programming (LP)
relaxations for the problem, and show highly scalable algorithms for
learning using these relaxations. I will next introduce a simpler approach,
called ‘psuedo-max’  learning, and show that it is consistent for separable
problems, and has empirical performance that is similar to LP relaxations.

I will conclude by addressing the problem of finding the K best solutions in
such problems, and show a new class of relaxations that has theoretical
guarantees and works well in practice.

Based on joint work with: Ofer Meshi, David Sontag, Menachem Fromer and
Tommi Jaakkola.

Host:              Tamir Hazan, tamir at ttic.edu
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