Zoltán Szabó: talks.

Invited Talk:
Conference of the International Society for Non-Parametric Statistics (ISNPS), Salerno, Italy. Characteristic Tensor Product Kernels. [June 11-15, 2018]
The Characteristic Property of Tensor Product Kernels. Research Seminar of the Montefiore Institute, University of Liége. [abstract, abstract (PDF); Oct. 13, 2017]
CREST Statistics Seminar, ENSAE. [Oct. 9, 2017]
French Excellence Summer School. Manifold Learning and Classification for EEG Analysis. [slides; July 27, 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; 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]

CMAP seminar:
Adaptive linear-time nonparametric two-sample testing. [abstract, slides, code; Nov. 22, 2016]

'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 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]
...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]

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