Abstract
Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic quadratic bound (SQB) methods for maximum likelihood estimation based on partition function optimization. Batch methods based on the quadratic bound were recently proposed for this class of problems, and performed favorably in comparison to state-of-the-art techniques. Semistochastic methods fall in between batch algorithms, which use all the data, and stochastic gradient type methods, which use small random selections at each iteration. We build semistochastic quadratic bound-based methods, and prove both global convergence (to a stationary point) under very weak assumptions, and linear convergence rate under stronger assumptions on the objective. To make the proposed methods faster and more stable, we consider inexact subproblem minimization and batch-size selection schemes. The efficacy of SQB methods is demonstrated via comparison with several state-of-the-art techniques on commonly used datasets.
| Original language | English (US) |
|---|---|
| State | Published - Jan 1 2014 |
| Event | 2nd International Conference on Learning Representations, ICLR 2014 - Banff, Canada Duration: Apr 14 2014 → Apr 16 2014 |
Conference
| Conference | 2nd International Conference on Learning Representations, ICLR 2014 |
|---|---|
| Country | Canada |
| City | Banff |
| Period | 4/14/14 → 4/16/14 |
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ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics
- Education
Cite this
Semistochastic quadratic bound methods. / Aravkin, Aleksandr; Choromanska, Anna; Jebara, Tony; Kanevsky, Dimitri.
2014. Paper presented at 2nd International Conference on Learning Representations, ICLR 2014, Banff, Canada.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Semistochastic quadratic bound methods
AU - Aravkin, Aleksandr
AU - Choromanska, Anna
AU - Jebara, Tony
AU - Kanevsky, Dimitri
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic quadratic bound (SQB) methods for maximum likelihood estimation based on partition function optimization. Batch methods based on the quadratic bound were recently proposed for this class of problems, and performed favorably in comparison to state-of-the-art techniques. Semistochastic methods fall in between batch algorithms, which use all the data, and stochastic gradient type methods, which use small random selections at each iteration. We build semistochastic quadratic bound-based methods, and prove both global convergence (to a stationary point) under very weak assumptions, and linear convergence rate under stronger assumptions on the objective. To make the proposed methods faster and more stable, we consider inexact subproblem minimization and batch-size selection schemes. The efficacy of SQB methods is demonstrated via comparison with several state-of-the-art techniques on commonly used datasets.
AB - Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic quadratic bound (SQB) methods for maximum likelihood estimation based on partition function optimization. Batch methods based on the quadratic bound were recently proposed for this class of problems, and performed favorably in comparison to state-of-the-art techniques. Semistochastic methods fall in between batch algorithms, which use all the data, and stochastic gradient type methods, which use small random selections at each iteration. We build semistochastic quadratic bound-based methods, and prove both global convergence (to a stationary point) under very weak assumptions, and linear convergence rate under stronger assumptions on the objective. To make the proposed methods faster and more stable, we consider inexact subproblem minimization and batch-size selection schemes. The efficacy of SQB methods is demonstrated via comparison with several state-of-the-art techniques on commonly used datasets.
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