MLE-Struct: Bethe Learning of Graphical Models

Add to your list(s) Download to your calendar using vCal

• Kui Tang, Columbia University
• Friday 13 May 2016, 11:00-12:00
• Engineering Department, CBL Room BE-438.

If you have a question about this talk, please contact Adrian Weller.

Many machine learning tasks require fitting probabilistic models over structured objects, such as pixel grids, matchings, and graph edges. Maximum likelihood estimation (MLE) for such domains is challenging due to the intractability of computing partition functions. One can resort to approximate marginal inference in conjunction with gradient descent, but such algorithms require careful tuning. Alternatively, in frameworks such as the structured support vector machine (SVM-Struct), discriminative functions are learned by iteratively applying efficient maximum a posteriori (MAP) decoders. We introduce MLE -Struct, a method for learning discrete exponential family models using the Bethe approximation to the partition function. Remarkably, this problem can also be reduced to iterative (MAP) decoding. This connection emerges by combining the Bethe approximation with the Frank-Wolfe (FW) algorithm on a convex dual objective, which circumvents the intractable partition function. Our method can learn both generative and conditional models and is substantially faster and easier to implement than existing MLE approaches while relying only on the same black-box interface to MAP decoding as SVM -Struct. We perform competitively on problems in denoising, segmentation, matching, and new datasets of roommate assignments and news and financial time series.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

• Seminar
• All Talks (aka the CURE list)
• Biology
• CBL important
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge talks
• Chris Davis' list
• Creating transparent intact animal organs for high-resolution 3D deep-tissue imaging
• Engineering Department, CBL Room BE-438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ML
• Machine Learning @ CUED
• Machine Learning Summary
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• Trust & Technology Initiative - interesting events
• bld31
• dh539
• kt532's list
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.