Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs
In this paper, we study the problem of learning from weakly labeled data, where labels of the traini...
Incorporating invariances into a learning algorithm is a common problem in ma-chine learning. We pro...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirab...
This is an expanded version of the original document. The new appendix discusses previous works whos...
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, wh...
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, wh...
Support Vector Machine (SVM) is one of the most important class of machine learning models and algo...
Incorporating invariances into a learning algorithm is a common problem in machine learning. We prov...
Concave-Convex Procedure (CCCP) has been widely used to solve nonconvex d.c.(difference of convex fu...
Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the work...
Support vector machines (SVMs) have been a dominant machine learning technique for more than a decad...
<p>The rapid growth in data availability has led to modern large scale convex optimization problems ...
While classical kernel-based classifiers are based on a single kernel, in practice it is often des...
Thesis: Ph. D. in Mathematics and Operations Research, Massachusetts Institute of Technology, Depart...
In this paper, we study the problem of learning from weakly labeled data, where labels of the traini...
Incorporating invariances into a learning algorithm is a common problem in ma-chine learning. We pro...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirab...
This is an expanded version of the original document. The new appendix discusses previous works whos...
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, wh...
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, wh...
Support Vector Machine (SVM) is one of the most important class of machine learning models and algo...
Incorporating invariances into a learning algorithm is a common problem in machine learning. We prov...
Concave-Convex Procedure (CCCP) has been widely used to solve nonconvex d.c.(difference of convex fu...
Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the work...
Support vector machines (SVMs) have been a dominant machine learning technique for more than a decad...
<p>The rapid growth in data availability has led to modern large scale convex optimization problems ...
While classical kernel-based classifiers are based on a single kernel, in practice it is often des...
Thesis: Ph. D. in Mathematics and Operations Research, Massachusetts Institute of Technology, Depart...
In this paper, we study the problem of learning from weakly labeled data, where labels of the traini...
Incorporating invariances into a learning algorithm is a common problem in ma-chine learning. We pro...
Many important machine learning problems are modeled and solved via semidefinite programs; examples ...
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