ICML

By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a ...

Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalization guarantees. In contrast, Page-rank variants are based on Markovian ...

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization ...

We consider a setting for discriminative semi-supervised learning where unlabeled data are used with a generative model to learn effective feature representations for discriminative training. Within this framework, we revisit the two-view feature ...

The L-BFGS limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L₂ regularization, but it cannot be used for an L₁-regularized loss due to its non-differentiability whenever ...

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core ...

We consider the problem of multiclass classification where both labeled and unlabeled data points are given. We introduce and demonstrate a new approach for estimating a distribution over the missing labels where data points are viewed as nodes of a ...

In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, ...

We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in information theoretic terms, as the gain in coding length when shifting from independent ...

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. ...

We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution ...

Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optimization algorithms that rely on the full gradient are not effective ...

Given observed data and a collection of parameterized candidate models, a 1 -- α confidence region in parameter space provides useful insight as to those models which are a good fit to the data, all while keeping the probability of incorrect exclusion ...

We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other SVM-based MIL methods, our approach more directly enforces the desired ...

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of ...

We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult problem because the dimension of a kernel space may be infinite. In the ...

The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank ...

We propose a local, generative model for similarity-based classification. The method is applicable to the case that only pairwise similarities between samples are available. The classifier models the local class-conditional distribution using a maximum ...

Although support vector machines (SVMs) for binary classification give rise to a decision rule that only relies on a subset of the training data points (support vectors), it will in general be based on all available features in the input space. We ...

We address feature selection problems for classification of small samples and high dimensionality. A practical example is microarray-based cancer classification problems, where sample size is typically less than 100 and number of features is several ...

We present an intuitive scheme for lossy color-image compression: Use the color information from a few representative pixels to learn a model which predicts color on the rest of the pixels. Now, storing the representative pixels and the image in ...

This paper studies the learning problem of ranking when one wishes not just to accurately predict pairwise ordering but also preserve the magnitude of the preferences or the difference between ratings, a problem motivated by its key importance in the ...

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse ...

Existing semi-supervised learning methods are mostly based on either the cluster assumption or the manifold assumption. In this paper, we propose an integrated regularization framework for semi-supervised kernel machines by incorporating both the ...

Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one ...

Clustering with constraints is a developing area of machine learning. Various papers have used constraints to enforce particular clusterings, seed clustering algorithms and even learn distance functions which are then used for clustering. We present ...

In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the ...

We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts binding affinity by using regression on substructures discovered by relational ...

Markov decision processes are an effective tool in modeling decision-making in uncertain dynamic environments. Since the parameters of these models are typically estimated from data, learned from experience, or designed by hand, it is not surprising ...

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact ...