Lista de obras - Alex Smola - Dominio Público Uruguay

A Generalized Representer Theorem

A Kernel Method for the Two-Sample-Problem ⬇️

artículo científico publicado en 2007

A Kernel Statistical Test of Independence ⬇️

artículo científico publicado en 2008

A Second Order Cone programming Formulation for Classifying Missing Data ⬇️

artículo científico publicado en 2005

A dependence maximization view of clustering

A tutorial on support vector regression

artículo científico publicado en 2004

Adapting Codes and Embeddings for Polychotomies ⬇️

artículo científico publicado en 2003

Automatic Chain of Thought Prompting in Large Language Models ⬇️

Bayesian Kernel Methods

Binet-Cauchy Kernels ⬇️

Bundle Methods for Machine Learning ⬇️

artículo científico publicado en 2008

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking ⬇️

artículo científico publicado en 2008

CoBaFi: collaborative bayesian filtering

Colored Maximum Variance Unfolding ⬇️

artículo científico publicado en 2008

Communication Efficient Distributed Machine Learning with the Parameter Server ⬇️

artículo científico publicado en 2014

Convex Learning with Invariances ⬇️

artículo científico publicado en 2008

Correcting Sample Selection Bias by Unlabeled Data ⬇️

artículo científico publicado en 2007

Covariate Shift by Kernel Mean Matching

Deep Sets ⬇️

Discriminative frequent subgraph mining with optimality guarantees

article

Distribution Matching for Transduction ⬇️

artículo científico publicado en 2009

Experimentally optimal nu in support vector regression for different noise models and parameter settings

artículo científico publicado en 2004

Fast Kernels for String and Tree Matching ⬇️

artículo científico publicado en 2003

Fast and Guaranteed Tensor Decomposition via Sketching ⬇️

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation ⬇️

FastEx: Hash Clustering with Exponential Families ⬇️

artículo científico publicado en 2012

Feature Hashing for Large Scale Multitask Learning ⬇️

artículo científico publicado en 2010

From Regularization Operators to Support Vector Kernels ⬇️

Gene selection via the BAHSIC family of algorithms

artículo científico publicado en 2007

Hyperkernels ⬇️

artículo científico publicado en 2003

Integrating structured biological data by Kernel Maximum Mean Discrepancy

artículo científico publicado en 2006

Invariant Feature Extraction and Classification in Kernel Spaces ⬇️

Kernel Machines and Boolean Functions ⬇️

Kernel Measures of Independence for non-iid Data ⬇️

Kernel PCA Pattern Reconstruction via Approximate Pre-Images

Kernel PCA and De-Noising in Feature Spaces ⬇️

artículo científico publicado en 1999

Kernel extrapolation

Kernel methods in machine learning

scientific article published in 2008

Kernelized Sorting ⬇️

Kernelized sorting

artículo científico publicado en 2010

Kernels and Regularization on Graphs

Laplace Propagation ⬇️

artículo científico publicado en 2004

Large-Scale Multiclass Transduction ⬇️

artículo científico publicado en 2006

Learning Networks of Heterogeneous Influence ⬇️

artículo científico publicado en 2012

Learning graph matching

artículo científico publicado en 2009

Learning with kernels: support vector machines, regularization, optimization, and beyond

libro publicado en 2002

Multitask Learning without Label Correspondences ⬇️

Near-optimal supervised feature selection among frequent subgraphs

article

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

artículo científico publicado en 1998

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants ⬇️

Online Learning with Kernels ⬇️

Optimal Web-Scale Tiering as a Flow Problem ⬇️

artículo científico publicado en 2010

Parallelized Stochastic Gradient Descent ⬇️

Prior Knowledge in Support Vector Kernels ⬇️

Protein function prediction via graph kernels

artículo científico publicado en 2005

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization ⬇️

artículo científico publicado en 2016

Regularization with Dot-Product Kernels ⬇️

Robust Near-Isometric Matching via Structured Learning of Graphical Models ⬇️

Semiparametric Support Vector and Linear Programming Machines ⬇️

Shrinking the Tube: A New Support Vector Regression Algorithm ⬇️

Simpler knowledge-based support vector machines

Slow Learners are Fast ⬇️

Sparse Greedy Gaussian Process Regression ⬇️

Spectral Methods for Indian Buffet Process Inference ⬇️

artículo científico publicado en 2014

Supervised feature selection via dependence estimation

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing ⬇️

artículo científico publicado en 1997

Support Vector Method for Novelty Detection ⬇️

artículo científico publicado en 2000

Support Vector Regression Machines ⬇️

artículo científico publicado en 1997

The Entropy Regularization Information Criterion ⬇️

The connection between regularization operators and support vector kernels.

artículo científico publicado en 1998

Tighter Bounds for Structured Estimation ⬇️

Transductive Gaussian Process Regression with Automatic Model Selection

Transformer on a Diet ⬇️

artículo científico publicado en 2020

Variance Reduction for Stochastic Gradient Optimization ⬇️

Variance Reduction in Stochastic Gradient Langevin Dynamics. ⬇️

artículo científico

Lista de obras de Alex Smola

A Generalized Representer Theorem

A Kernel Method for the Two-Sample-Problem ⬇️

A Kernel Statistical Test of Independence ⬇️

A Second Order Cone programming Formulation for Classifying Missing Data ⬇️

A dependence maximization view of clustering

A tutorial on support vector regression

Adapting Codes and Embeddings for Polychotomies ⬇️

Automatic Chain of Thought Prompting in Large Language Models ⬇️

Bayesian Kernel Methods

Binet-Cauchy Kernels ⬇️

Bundle Methods for Machine Learning ⬇️

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking ⬇️

CoBaFi: collaborative bayesian filtering

Colored Maximum Variance Unfolding ⬇️

Communication Efficient Distributed Machine Learning with the Parameter Server ⬇️

Convex Learning with Invariances ⬇️

Correcting Sample Selection Bias by Unlabeled Data ⬇️

Covariate Shift by Kernel Mean Matching

Deep Sets ⬇️

Discriminative frequent subgraph mining with optimality guarantees

Distribution Matching for Transduction ⬇️

Experimentally optimal nu in support vector regression for different noise models and parameter settings

Fast Kernels for String and Tree Matching ⬇️

Fast and Guaranteed Tensor Decomposition via Sketching ⬇️

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation ⬇️

FastEx: Hash Clustering with Exponential Families ⬇️

Feature Hashing for Large Scale Multitask Learning ⬇️

From Regularization Operators to Support Vector Kernels ⬇️

Gene selection via the BAHSIC family of algorithms

Hyperkernels ⬇️

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Invariant Feature Extraction and Classification in Kernel Spaces ⬇️

Kernel Machines and Boolean Functions ⬇️

Kernel Measures of Independence for non-iid Data ⬇️

Kernel PCA Pattern Reconstruction via Approximate Pre-Images

Kernel PCA and De-Noising in Feature Spaces ⬇️

Kernel extrapolation

Kernel methods in machine learning

Kernelized Sorting ⬇️

Kernelized sorting

Kernels and Regularization on Graphs

Laplace Propagation ⬇️

Large-Scale Multiclass Transduction ⬇️

Learning Networks of Heterogeneous Influence ⬇️

Learning graph matching

Learning with kernels: support vector machines, regularization, optimization, and beyond

Multitask Learning without Label Correspondences ⬇️

Near-optimal supervised feature selection among frequent subgraphs

Nonlinear Component Analysis as a Kernel Eigenvalue Problem

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants ⬇️

Online Learning with Kernels ⬇️

Optimal Web-Scale Tiering as a Flow Problem ⬇️

Parallelized Stochastic Gradient Descent ⬇️

Prior Knowledge in Support Vector Kernels ⬇️

Protein function prediction via graph kernels

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization ⬇️

Regularization with Dot-Product Kernels ⬇️

Robust Near-Isometric Matching via Structured Learning of Graphical Models ⬇️

Semiparametric Support Vector and Linear Programming Machines ⬇️

Shrinking the Tube: A New Support Vector Regression Algorithm ⬇️

Simpler knowledge-based support vector machines

Slow Learners are Fast ⬇️

Sparse Greedy Gaussian Process Regression ⬇️

Spectral Methods for Indian Buffet Process Inference ⬇️

Supervised feature selection via dependence estimation

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing ⬇️

Support Vector Method for Novelty Detection ⬇️

Support Vector Regression Machines ⬇️

The Entropy Regularization Information Criterion ⬇️

The connection between regularization operators and support vector kernels.

Tighter Bounds for Structured Estimation ⬇️

Transductive Gaussian Process Regression with Automatic Model Selection

Transformer on a Diet ⬇️

Variance Reduction for Stochastic Gradient Optimization ⬇️

Variance Reduction in Stochastic Gradient Langevin Dynamics. ⬇️

Word Features for Latent Dirichlet Allocation ⬇️

kernlab- AnS4Package for Kernel Methods inR

v-Arc: Ensemble Learning in the Presence of Outliers ⬇️