Lower Bounds for BMRM and Faster Rates for Training SVMs (2009)
Saha, Ankan, Zhang, Xinhua, Vishwanathan, S. V. N.
Regularized risk minimization with the binary hinge loss and its variants lies at the heart of many machine learning problems. Bundle methods for regularized risk minimization (BMRM) and the closely...
Efficient Approximation Algorithms for Minimum Enclosing Convex Shapes (2009)
Saha, Ankan, Vishwanathan, S. V. N.
We address the problem of Minimum Enclosing Ball (MEB) and its generalization to Minimum Enclosing Convex Polytope (MECP). Given $n$ points in a $d$ dimensional Euclidean space, we give a...
Variable Metric Stochastic Approximation Theory (2009)
Sunehag, Peter, Trumpf, Jochen, Vishwanathan, S. V. N., Schraudolph, Nicol
We provide a variable metric stochastic approximation theory. In doing so, we provide a convergence theory for a large class of online variable metric methods including the recently introduced online...
Efficient Graphlet Kernels for Large Graph Comparison (2008)
Shervashidze, Nino, Vishwanathan, S.V.N., Petri, Tobias H., Mehlhorn, Kurt, Borgwardt, Karsten M.
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we attempt to rectify this situation. We compare graphs by counting common...
Efficient Graphlet Kernels for Large Graph Comparison (2008)
Shervashidze, Nino, Vishwanathan, S V N, Petri, Tobias, Mehlhorn, Kurt, Borgwardt, Karsten
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting common {\it graphlets}, \ie...
Vishwanathan, S. V. N., Borgwardt, Karsten M., Kondor, Imre Risi, Schraudolph, Nicol N.
We present a unified framework to study graph kernels, special cases of which include the random walk graph kernel \citep{GaeFlaWro03,BorOngSchVisetal05}, marginalized graph kernel...
A Quasi-Newton Approach to Nonsmooth Convex Optimization (2008)
Yu, Jin, Vishwanathan, S. V. N., Guenter, Simon, Schraudolph, Nicol N.
We extend the well-known BFGS quasi-Newton method and its limited-memory variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by generalizing three...
Fast Iterative Kernel Principal Component Analysis (2007)
Guenter, Simon, Schraudolph, Nicol, Vishwanathan, S V N
We develop gain adaptation methods that improve convergence of the kernel Hebbian algorithm (KHA) for iterative kernel PCA (Kim et al., 2005). KHA has a scalar gain parameter which is either held...
A scalable modular convex solver for regularized risk minimization (2007)
Teo, Choon Hui, Smola, Alex, Vishwanathan, S V N
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers....
Semi-Markov Models for Sequence Segmentation (2007)
Shi, Qinfeng, Altun, Yasemin, Smola, Alex, Vishwanathan, S V N
In this paper, we study the problem of automatically segmenting written text into paragraphs. This is inherently a sequence labeling problem, however, previous approaches ignore this dependency. We...
Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
Graph Kernels for Disease Outcome Prediction from Protein-Protein Interaction Networks (2007)
Borgwardt, KM, Kriegel, HP, Vishwanathan, S V N, Schraudolph, Nicol
It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently...
Fast Computation of Graph Kernels (2007)
Vishwanathan, S V N, Borgwardt, Karsten, Schraudolph, Nicol
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us...
Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
CLASS PREDICTION FROM TIME SERIES GENE EXPRESSION PROFILES USING DYNAMICAL SYSTEMS KERNELS (2006)
Borgwardt, Karsten, Vishwanathan, S V N, Kriegel, Hans-Peter
We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal...
Vishwanathan, S V N, Borgwardt, K. M., Guttman, Omri, Smola, Alex
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine...
Step Size Adaptation in Reproducing Kernel Hilbert Space (2005)
Vishwanathan, S V N, Schraudolph, Nicol, Smola, Alex
This paper presents an online Support Vector Machine (SVM) that uses the Stochastic Meta-Descent (SMD) algorithm to adapt its step size automatically. We formulate the online learning problem as a...
Vishwanathan, S V N, Smola, Alex, Vidal, Rene
We derive a family of kernels on dynamical systems by applying the Binet-Cauchy theorem to trajectories of states. Our derivation provides a unifying framework for all kernels on dynamical systems...
Large Protein function prediction via faster graph kernels (2005)
Borgwardt, Karsten, Vishwanathan, S V N, Schraudolph, Nicol, Kriegel, Hans-Peter
Kernel functions on graphs have been defined over recent years. In earlier work, we have employed random walk graph kernels for predicting protein function from graph representations that integrate...
Vishwanathan, S V N, Smola, Alex, Schraudolph, Nicol
We present a fast iterative support vector training algorithm for the quadratic hard margin formulation. Our algorithm works by incrementally changing a candidate support vector set using a locally...
Vishwanathan, S V N, Borgwardt, Karsten, Guttman, Omri, Smola, Alex
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine...
Learnability of Probabilistic Automata via Oracles (2005)
Guttman, Omri, Vishwanathan, S V N, Williamson, Bob
Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed $\mu$-distinguishable. In this paper, we prove that state merging algorithms can be...
Step Size-Adapted Online Support Vector Learning (2005)
Karatzoglou, Alexandros, Vishwanathan, S V N, Schraudolph, Nicol N., Smola, Alex
We present an online Support Vector Machine (SVM) that uses Stochastic Meta-Descent (SMD) to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient...
Warmuth, Manfred, Vishwanathan, S V N
We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds even if we allow...
Kernel Methods for Missing Variables (2005)
Smola, Alex, Vishwanathan, S V N, Hoffman, Thomas
We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ignored by kernel...
Invariances in Classification : an efficient SVM implementation (2005)
Loosli, Gaëlle, Canu, Stéphane, Vishwanathan, S V N, Smola, Alex
Often, in pattern recognition, complementary knowledge is available. This could be useful to improve the performance of the recognition system. Part of this knowledge regards invariances, in...
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten M., Ong, Cheng Soon, Schoenauer, Stefan, Vishwanathan, S V N, Smola, Alex, Kriegel, Hans-Peter
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid...
Large-Scale Multiclass Transduction (2005)
Geartner, Thomas, Le, Quoc, Burton, Simon, Smola, Alex, Vishwanathan, S V N
We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel...
Kernel methods for missing variables (2005)
Smola, Alex, Vishwanathan, S V N, Hofmann, Thomas
We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ig- nored by kernel...
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten, Ong, Cheng Soon, Schonauer, Stefan, Vishwanathan, S V N, Smola, Alex, Kriegel, Hans-Peter
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid...
Learnability of Probabilistic Automata via Oracles (2005)
Guttmann, Omri, Vishwanathan, S V N, Williamson, Bob
Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed µ-distinguishable. In this paper, we prove that state merging algorithms can be...
Large-scale multiclass transduction (2005)
Gaertner, Thomas, Le, Quoc, Burton, Simon, Smola, Alex, Vishwanathan, S V N
We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel...
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten M., Ong, Cheng Soon, Schönauer, Stefan, Vishwanathan, S. V. N., Smola, Alex J., Kriegel, Hans-Peter
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid...
Une boîte à outils rapide et simple pour les SVM (2004)
Loosli, Gaëlle, Canu, Stéphane, Vishwanathan, S V N, Smola, Alex, Chattopadhyay, Manojit
If SVM (Support Vector Machines) is now considered as one of the best learning methods, it is still considered as slow. Here we propose a Matlab toolbox that enables the usage of SVM in a fast and...
A fast and efficient toolbox for SVMs in Matlab (2004)
Loosli, Gaëlle, Canu, Stéphane, Rakotomamonjy, Alain, Smola, Alex, Vishwanathan, S V N
Technology: The presented Toolbox is an efficient implementation in MATLAB of the SimpleSVM algorithm. It provides the exact solution to the SVM problem. The extended version of the toolbox provides...
Vishwanathan, S V N, Smola, Alex
We propose a family of kernels based on the Binet-Cauchy theorem and its ex- tension to Fredholm operators. This includes as special cases all currently known kernels derived from the behavioral...
Fast kernels for string and tree matching (2004)
Vishwanathan, S V N, Smola, Alex
In this chapter we present a new algorithm suitable for matching discrete ob jects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. This...
Kernel Methods Fast Algorithms and real life applications (2003)
Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is...
Kernel Methods Fast Algorithms and real life applications (2003)
Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is...
Kernel Methods Fast Algorithms and real life applications (2003)
Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is...
Kernel Methods Fast Algorithms and real life applications (2003)
Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is...