**Zoltán Szabó**: recent talks.

Invited Talk:

Statistical session at the joint meeting of the Italian Mathematical Union, Italian Society of Industrial and Applied Mathematics & Polish Mathematical Society [Sept. 17-20, 2018]

EPFL: Laboratory for Information and Inference Systems (LIONS). HSIC, A Measure of Independence?
[abstract, slides; Feb. 28, 2018]

ETH Zürich: Department of Biosystems Science and Engineering (D-BSSE): Machine Learning & Computational Biology Lab. HSIC, An Independence Measure? [slides; Feb. 26, 2018]

INRIA Saclay: Tao Seminar. Linear-time Divergence Measures with Applications in Hypothesis Testing. [abstract, slides; Feb. 13, 2018].

Pennsylvania State University: Department of Statistics. Characterizing Independence with Tensor Product Kernels. [slides; Dec. 13, 2017]

Google Brain, Mountain View. Tensor Product Kernels: Independence and Beyond. [abstract, slides; Dec. 1, 2017]

Cubist Systematic Strategies: Advanced Methods Group, New York. Tensor Product Kernels: Characteristic Property and Beyond. [abstract, slides; Nov. 28, 2017]

Yahoo Research, New York. Independence with Tensor Product Kernels. [abstract, slides; Nov. 28, 2017]

ETH Zürich: SfS: Research Seminar. Tensor Product Kernels: Characteristic Property and Universality. [abstract, slides; Nov. 3, 2017]

Télécom ParisTech: PASADENA Seminar. Data-Efficient Independence Testing with Analytic Kernel Embeddings. [abstract, slides, code; May 17, 2017]

Henri Poincaré Institute: Parisian Statistics Seminar. Distribution Regression: A Simple Technique with Minimax-optimal
Guarantee. [abstract, slides, code; Mar. 27, 2017]

Marseilles: Signal Processing and Machine Learning Seminar. A linear-time adaptive nonparametric two-sample test. [abstract, slides, code; Mar. 24, 2017]

Orsay: Probability and Statistics Seminar. Minimax-optimal Distribution Regression. [abstract, slides, code; Mar. 16, 2017]

Télécom ParisTech: Machine Learning Seminar. T-testing: A Linear-time Adaptive Nonparametric Technique. [abstract, slides, code; Feb. 2, 2017]

Dagstuhl Seminar: New Directions for Learning with Kernels and Gaussian Processes. Distribution regression. [slides, code, Dagstuhl report; Dec. 1, 2016]

Facebook AI Research. Adaptive linear-time nonparametric t-test. [abstract, slides, code; Nov. 21, 2016]

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]

MASCOT-NUM 2016 @ Institut de Mathématiques de Toulouse, INSA Toulouse. Distribution Regression with Minimax-Optimal Guarantee. [abstract, slides, code; Mar. 23-25, 2016]

MPI, Tübingen: Special Symposium on Intelligent Systems.
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]

CMStatistics 2015. Learning Theory for Vector-Valued Distribution Regression. [abstract, slides, code; Dec. 12, 2015]

Pennsylvania State University. Optimal Uniform and Lp Rates for Random Fourier Features. [slides; Dec. 4, 2015]

Carnegie Mellon University: Statistical ML Reading Group. Optimal Rates for the Random Fourier Feature Method. [abstract, slides; Dec. 1, 2015]

Carnegie Mellon University: ML Lunch Seminar. Distribution Regression: Computational and Statistical Tradeoffs. [abstract, slides, code; Nov. 30, 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]

University of Sheffield: ML@SITraN group. Performance Guarantees for Random Fourier Features - Limitations and Merits. [abstract, slides; arXiv: abstract, paper; June 25-26, 2015]

University of Warwick: Department of Statistics: Centre for Research in Statistical Methodology (CRiSM) Seminars. Regression on Probability Measures: A Simple and Consistent Algorithm. [event, abstract, slides, code; May 29, 2015]

University of Oxford: Department of Statistics: Computational Statistics and Machine Learning reading group. Vector-valued Distribution Regression - Keep It Simple and Consistent. [event, abstract, slides, code; May 1, 2015]

University of Birmingham: Artificial Intelligence and Natural Computation seminars. A Simple and Consistent Technique for Vector-valued Distribution Regression. [event, abstract, slides, code; Jan. 26, 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]

UCL: Statistical Science Seminars: Vector-valued Distribution Regression. A Simple and Consistent Approach. [abstract, slides, code; Oct. 9, 2014]

UCL: CSML Lunch Talk Series. Distribution Regression - the Set Kernel Heuristic is Consistent. [event, abstract, slides, paper, code; May 2, 2014]

University of Hertfordshire: Computer Science Research Colloquium. Consistent Distribution Regression via Mean Embedding. [abstract, slides, paper, code; Mar. 5, 2014]

'Statistics with coffee' seminar (CMAP):

Random Fourier Features: Optimal Uniform Bounds. [slides, Oct. 5, 2016]

Machine Learning Journal Club (CMAP):

Examples are not enough, learn to criticize! Criticism for Interpretability. [May 4, 2017]

Research Talk (Gatsby):

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]

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]

...k [slides; June 19, 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]

Machine Learning Journal Club (Gatsby):

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]

Kernel Reading Group (Oxford):

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]