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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks

March 15, 2023

Authors

Li Chou

The University of Texas at Dallas


Pracheta Sahoo

The University of Texas at Dallas


Somdeb Sarkhel

Adobe Research


Nicholas Ruozzi

The University of Texas at Dallas


Vibhav Gogate

The University of Texas at Dallas


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

AAAI Technical Track: Machine Learning

Downloads:

Download PDF

Abstract:

Parameter tying is a regularization method in which parameters (weights) of a machine learning model are partitioned into groups by leveraging prior knowledge and all parameters in each group are constrained to take the same value. In this paper, we consider the problem of parameter learning in Markov networks and propose a novel approach called automatic parameter tying (APT) that uses automatic instead of a priori and soft instead of hard parameter tying as a regularization method to alleviate overfitting. The key idea behind APT is to set up the learning problem as the task of finding parameters and groupings of parameters such that the likelihood plus a regularization term is maximized. The regularization term penalizes models where parameter values deviate from their group mean parameter value. We propose and use a block coordinate ascent algorithm to solve the optimization task. We analyze the sample complexity of our new learning algorithm and show that it yields optimal parameters with high probability when the groups are well separated. Experimentally, we show that our method improves upon L2 regularization and suggest several pragmatic techniques for good practical performance.

DOI:

10.1609/aaai.v32i1.11785


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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