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Search: smola - Matches: 21

Author:
 User iconAlexander J. Smola
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Invited talks:
 invited talk Alexander J. Smola: Fast Food: Approximating Kernel Expansion in Loglinear Time
The ability to evaluate nonlinear function classes rapidly is crucial for nonparametric estimation. We propose an improvement to random kitchen sinks that offers O(n log d) computation and O(n) storage for n basis functions in d dimensions without sacrificing accuracy. ...
 invited talk Alexander J. Smola: Learning Graph Matching
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes ...
 invited talk Alexander J. Smola: Scaling Latent Variable Models
 invited talk Alexander J. Smola: The Parameter Server
In this talk I will discuss a number of vignettes on scaling optimization and inference. Despite arising from very different contexts (graphical models inference, convex optimization, neural networks), they all share a common design pattern - a synchronization mechanism in ...

Lectures:
 lecture Alexander J. Smola: Bayesian Kernel Methods
 lecture Yee Whye Teh: Discussion of Alex Smola's talk: Remarks on parallelised MCMC
 lecture Alexander J. Smola: Exponential Families in Feature Space
In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We ...
 lecture Alexander J. Smola: Exponential Families in Feature Space
In this course I will discuss how exponential families, a standard tool in statistics, can be used with great success in machine learning to unify many existing algorithms and to invent novel ones quite effortlessly. In particular, I will show ...
 lecture Alexander J. Smola: From collaborative filtering to multitask learning
Recent work on collaborative filtering has led to a large number of both scalable and theoretically well founded algorithms. In this paper, we show that collaborative filtering and multitask learning are innately closely connected. In particular, the 'learning the kernel' ...
 lecture Alexander J. Smola: Introduction to kernel methods
This lecture given by Mr. Smola is combined with Mr. Bernhard Schoelkopf and will encopass Part 1, Part 5, Part 6 of the complete lecture. Part 2, 3 and 4 of this lecture can be found here at Bernhard Schoelkopf's ...
 lecture Alexander J. Smola: Kernel Methods
In this short course I will discuss exponential families, density estimation, and conditional estimators such as Gaussian Process classification, regression, and conditional random fields. The key point is that I will be providing a unified view of these estimation methods. ...
 lecture Alexander J. Smola: Mixed Norm Kernels, Hyperkernels and Other Variants
 lecture Alexander J. Smola: Nonparametric Tests between Distributions
Reproducing Kernel Hilbert Spaces have been mainly used for estimation. Distributional tests in this area were mainly concerned with tests for independence of random variables. We give concentration of measure bounds for the latter using an easy to compute criterion ...
 lecture Alexander J. Smola: Parallel Topic Models
 lecture Alexander J. Smola: Unifying Divergence Minimization and Statistical Inference via Convex Duality
We unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate maximum entropy estimation is maximum a posteriori estimation. Moreover, our treatment leads to stability and ...
 lecture Alexander J. Smola: Unsupervised Learning with Kernels
 lecture Alexander J. Smola: Using features of probability distributions to achieve covariate shift

Panel:
 panel Shai Ben-David, Edwin Hancock, Alexander J. Smola, Joachim M. Buhmann: Is non-(geo)metricity an issue for machine learning?

Tutorials:
 tutorial Alexander J. Smola: Exponential Families
In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We ...
 tutorial Alexander J. Smola: Kernel methods and Support Vector Machines
The tutorial will introduce the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over ...