Smola, Alexander J.
Alexander J. Smola machine learning scientist
VIAF ID: 72123027 (Personal)
Permalink: http://viaf.org/viaf/72123027
Preferred Forms
- 100 0 _ ‡a Alexander J. Smola ‡c machine learning scientist
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- 100 1 _ ‡a Smola, Alexander J.
- 100 1 _ ‡a Smola, Alexander J.
- 100 1 _ ‡a Smola, Alexander J.
- 100 1 _ ‡a Smola, Alexander J.
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- 100 1 0 ‡a Smola, Alexander J.
4xx's: Alternate Name Forms (6)
5xx's: Related Names (2)
Works
Title | Sources |
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Adapting Codes and Embeddings for Polychotomies | |
Advanced lectures on machine learning : Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002 : revised lectures | |
Advances in kernel methods, 1998: | |
Advances in large margin classifiers | |
Automatic Chain of Thought Prompting in Large Language Models | |
Binet-Cauchy Kernels | |
Bundle Methods for Machine Learning | |
CoBaFi: collaborative bayesian filtering | |
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking | |
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 | |
A dependence maximization view of clustering | |
Discriminative frequent subgraph mining with optimality guarantees | |
Distribution Matching for Transduction | |
Dive into deep learning | |
The Entropy Regularization Information Criterion | |
Experimentally optimal nu in support vector regression for different noise models and parameter settings | |
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation | |
Fast and Guaranteed Tensor Decomposition via Sketching | |
Fast Kernels for String and Tree Matching | |
FastEx: Hash Clustering with Exponential Families | |
Feature Hashing for Large Scale Multitask Learning | |
Gene selection via the BAHSIC family of algorithms. | |
A Generalized Representer Theorem | |
Hyperkernels | |
Integrating structured biological data by Kernel Maximum Mean Discrepancy | |
Kernel extrapolation | |
Kernel Machines and Boolean Functions | |
Kernel Measures of Independence for non-iid Data | |
A Kernel Method for the Two-Sample-Problem | |
Kernel PCA and De-Noising in Feature Spaces | |
Kernel PCA Pattern Reconstruction via Approximate Pre-Images | |
A Kernel Statistical Test of Independence | |
Kernelized Sorting | |
kernlab- AnS4Package for Kernel Methods inR | |
Laplace Propagation | |
Large-Scale Multiclass Transduction | |
Learning Networks of Heterogeneous Influence | |
Learning with kernels : support vector machines, regularization, optimization, and beyond | |
Multitask Learning without Label Correspondences | |
Nonlinear Component Analysis as a Kernel Eigenvalue Problem | |
Optimal Web-Scale Tiering as a Flow Problem | |
Parallelized Stochastic Gradient Descent | |
Protein function prediction via graph kernels. | |
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization | |
Robust Near-Isometric Matching via Structured Learning of Graphical Models | |
A Second Order Cone programming Formulation for Classifying Missing Data | |
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 | |
Spectral Methods for Indian Buffet Process Inference | |
Supervised feature selection via dependence estimation | |
Support Vector Method for Novelty Detection | |
Tighter Bounds for Structured Estimation | |
Transductive Gaussian Process Regression with Automatic Model Selection | |
Transformer on a Diet | |
v-Arc: Ensemble Learning in the Presence of Outliers | |
Variance Reduction in Stochastic Gradient Langevin Dynamics. | |
Word Features for Latent Dirichlet Allocation |