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Vojtěch Franc

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NeurIPS Conference 2024 Conference Paper

Constrained Binary Decision Making

Daniel Průša
Vojtěch Franc

Binary statistical decision making involves choosing between two states based on statistical evidence. The optimal decision strategy is typically formulated through a constrained optimization problem, where both the objective and constraints are expressed as integrals involving two Lebesgue measurable functions, one of which represents the strategy being optimized. In this work, we present a comprehensive formulation of the binary decision making problem and provide a detailed characterization of the optimal solution. Our framework encompasses a wide range of well-known and recently proposed decision making problems as specific cases. We demonstrate how our generic approach can be used to derive the optimal decision strategies for these diverse instances. Our results offer a robust mathematical tool that simplifies the process of solving both existing and novel formulations of binary decision making problems which are in the core of many Machine Learning algorithms.

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JMLR Journal 2010 Journal Article

The SHOGUN Machine Learning Toolbox

Sören Sonnenburg
Gunnar Rätsch
Sebastian Henschel
Christian Widmer
Jonas Behr
Alexander Zien
Fabio de Bona
Alexander Binder

We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more. Most of the specific algorithms are able to deal with several different data classes. We have used this toolbox in several applications from computational biology, some of them coming with no less than 50 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond. SHOGUN is implemented in C++ and interfaces to MATLAB TM, R, Octave, Python, and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

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JMLR Journal 2009 Journal Article

Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization

Vojtěch Franc
Sören Sonnenburg

We have developed an optimized cutting plane algorithm (OCA) for solving large-scale risk minimization problems. We prove that the number of iterations OCA requires to converge to a ε precise solution is approximately linear in the sample size. We also derive OCAS, an OCA-based linear binary Support Vector Machine (SVM) solver, and OCAM, a linear multi-class SVM solver. In an extensive empirical evaluation we show that OCAS outperforms current state-of-the-art SVM solvers like SVM light, SVM perf and BMRM, achieving speedup factor more than 1,200 over SVM light on some data sets and speedup factor of 29 over SVM perf, while obtaining the same precise support vector solution. OCAS, even in the early optimization steps, often shows faster convergence than the currently prevailing approximative methods in this domain, SGD and Pegasos. In addition, our proposed linear multi-class SVM solver, OCAM, achieves speedups of factor of up to 10 compared to SVM multi-class. Finally, we use OCAS and OCAM in two real-world applications, the problem of human acceptor splice site detection and malware detection. Effectively parallelizing OCAS, we achieve state-of-the-art results on an acceptor splice site recognition problem only by being able to learn from all the available 50 million examples in a 12-million-dimensional feature space. Source code, data sets and scripts to reproduce the experiments are available at http://cmp.felk.cvut.cz/~xfrancv/ocas/html/. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

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JMLR Journal 2008 Journal Article

Discriminative Learning of Max-Sum Classifiers

Vojtěch Franc
Bogdan Savchynskyy

The max-sum classifier predicts n -tuple of labels from n -tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and observable variables. Predicting labels as MAP assignments of a Random Markov Field is a particular example of the max-sum classifier. Learning parameters of the max-sum classifier is a challenging problem because even computing the response of such classifier is NP-complete in general. Estimating parameters using the Maximum Likelihood approach is feasible only for a subclass of max-sum classifiers with an acyclic structure of neighbouring pairs. Recently, the discriminative methods represented by the perceptron and the Support Vector Machines, originally designed for binary linear classifiers, have been extended for learning some subclasses of the max-sum classifier. Besides the max-sum classifiers with the acyclic neighbouring structure, it has been shown that the discriminative learning is possible even with arbitrary neighbouring structure provided the quality functions fulfill some additional constraints. In this article, we extend the discriminative approach to other three classes of max-sum classifiers with an arbitrary neighbourhood structure. We derive learning algorithms for two subclasses of max-sum classifiers whose response can be computed in polynomial time: (i) the max-sum classifiers with supermodular quality functions and (ii) the max-sum classifiers whose response can be computed exactly by a linear programming relaxation. Moreover, we show that the learning problem can be approximately solved even for a general max-sum classifier. [abs] [ pdf ][ bib ] &copy JMLR 2008. ( edit, beta )

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