I have moved to France (Sept., 2016); my
new webpage.
Zoltán Szabó: talks.
Invited Talk:
Realeyes, Budapest, Hungary. Distinguishing Distributions with Maximum Testing Power. [
slides,
code; Aug. 24, 2016]
eResearch Domain launch event, London, UK. Optimal Regression on Sets. [
poster; June 29, 2016]
International Workshop on Pattern Recognition in Neuroimaging (
PRNI), Trento, Italy. Hypothesis Testing with Kernels. [
abstract,
slides; June 22-24, 2016]
University of California, San Diego. Kernel-based learning on probability distributions. [
slides,
code; Apr. 25, 2016]
Special Symposium on Intelligent Systems (MPI, Tübingen). Performance guarantees for kernel-based learning on probability distributions. [
abstract,
slides,
code; Mar. 15-16, 2016]
École Polytechnique. Optimal Rates for the Random Fourier Feature Technique. [
abstract,
slides; Mar. 14, 2016]
Imperial College London: Department of Computing. Learning from Features of Sets and Probabilities. [
abstract,
slides; Mar. 9, 2016]
Pennsylvania State University. Optimal Uniform and Lp Rates for Random Fourier Features. [
slides; Dec. 4, 2015]
Princeton University. Distribution Regression: Computational and Statistical Tradeoffs. [
abstract,
slides,
code; Nov. 26-27, 2015]
University of Alberta. Optimal Rates for Random Fourier Feature Approximations. [
abstract,
slides; Nov. 23-24, 2015]
UC Berkeley: AMPLab. Optimal Rates for Random Fourier Feature Kernel Approximations. [
abstract,
slides; Nov. 20, 2015]
Max Planck Institute for Intelligent Systems (Tübingen):
Bernhard Schölkopf's lab. Consistent Vector-valued Regression on Probability Measures. [
abstract,
slides,
code; Jan. 14-18, 2015]
Research Talk (Gatsby):
Optimal Distribution Regression [
slides,
code; May 23, 2016]
Optimal Uniform and Lp Rates for Random Fourier Features [
slides; see also arXiv:
abstract,
paper; Sept. 7, 2015]
Optimal Rate for Random Kitchen Sinks - Journey to Empirical Process Land [
slides; May 18, 2015]
Distribution-to-Anything Regression [
slides,
code; Sept. 8, 2014]
Consistent, Two-Stage Sampled Distribution Regression [
slides,
paper,
code; Mar. 10, 2014]
Tea Talk (Gatsby):
9 Initialization Strategies [
slides; June 28, 2016]
Nim & Friends [
slides; Jan. 12, 2016]
The Khintchine Constant and Friends [
slides; Sept. 18, 2015]
Supervised Descent Method and its Applications to Face Alignment [
slides; Mar. 16, 2015]
Word Storms: Multiples of Word Clouds for Visual Comparison of Documents [
slides; Dec. 18, 2014]
The Dvorak Element of the Symmetric Group [
slides; Aug. 15, 2014]
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates [
slides; June 10, 2014]
Wasserstein Propagation for Semi-Supervised Learning [
slides; Mar. 21, 2014]
Rubik’s on the Torus [
slides; Feb. 20, 2014]
On the Chi Square and Higher-Order Chi Distances for Approximating f-Divergences [
slides; Dec. 20, 2013]
Smoothing Proximal Gradient Method for General Structured Sparse Regression [
slides; Oct. 25, 2013]
Characterizing the Representer Theorem [
slides; Oct. 3, 2013]
Statistical Depth Function [
slides; June 26, 2016]
Nonparametric Independence Testing for Small Sample Sizes [
slides; Apr. 4, 2016]
Autodiff [
slides; Jan. 12, 2016]
Kernels Based Tests with Non-asymptotic Bootstrap Approaches for Two-sample Problems [
slides; Nov. 2, 2015]
Random Kitchen Sinks - Revisited [
slides; Mar. 12, 2015]
Elementary Estimators for High-Dimensional Linear Regression [
slides; Nov. 24, 2014]
Scalable Kernel Methods via Doubly Stochastic Gradients [
slides; Oct. 20, 2014]
Fastfood - Approximating Kernel Expansions in Loglinear Time [
slides; May 16, 2014]
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least squares [Aug. 17, 2015]
Quinquennial Review Symposium (Gatsby):
Optimal Uniform and Lp Rates for Random Fourier Features [
poster; Sept. 23, 2015]
Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM) [
poster; Sept. 23, 2015]
External Review (Gatsby):
Two-Stage Sampled Distribution Regression on Separable Topological Domains [
poster; Oct. 29, 2014]
Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM) [
poster; Oct. 29, 2014]