Zoltán Szabó

Center for Applied Mathematics (CMAP), #00 3017
École Polytechnique [on Google Maps; local time: ]
Route de Saclay, 91128 Palaiseau, France
Email: zoltan (dot) szabo (at) polytechnique (dot) edu
Zoltán Szabó
(photo credit: Peter Richtárik)
I am a Research Associate Professor at CMAP & DSI, École Polytechnique. My main research interests are information theory (ITE), kernel methods, statistical machine learning, empirical processes, with occasional excursions in remote sensing (sustainability), distribution regression, hypothesis testing, structured sparsity, independent subspace analysis and its extensions, collaborative filtering, face emotion recognition and face tracking, natural language processing.
Latest News:

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2018
July Talk @ French Excellence Summer School
June 11-15 Talk @ Conference of the International Society for Non-Parametric Statistics
Apr. 3-5 At: DALI-2018
Feb. - Mar. Service: Area Chair @ ICML-2018
Feb. 26 Talk @ Machine Learning & Computational Biology Lab, ETH Zürich
Spring Lecturing: Structured Data: Learning, Prediction, Dependency, Testing
Spring Lecturing: Statistical Models in Biology and Physics
Spring Lecturing: Advanced Machine Learning
2017
Dec. 11-15 Visit @ Department of Statistics, Pennsylvania State University
Dec. 4-9 At: NIPS-2017.
Our submission got one of the 3 Best Paper Awards! (=top 0.09%)
[paper, code]

= adaptive linear-time nonparametric goodness-of-fit test

Dec. 8 Organizing: Learning on Distributions, Functions, Graphs and Groups workshop @ NIPS-2017.
Dec. 1 Talk @ Google Brain, Mountain View
[abstract]
Nov. - Dec. Service: Area Chair @ AISTATS-2018
Nov. 29 Visit @ Department of Statistics, Columbia University
Nov. 28 Talk @ Advanced Methods Group, Cubist Systematic Strategies
[abstract]
Nov. 28 Talk @ Yahoo Research, New York
[abstract]
Nov. 27 Talk @ Machine Learning Department, Carnegie Mellon University
Nov. 14 At: Random matrix advances in large dimensional statistics and machine learning day
Nov. 3 Talk @ Research Seminar, SfS, ETH Zürich
[abstract, slides]
Oct. - Nov. Service: reviewing grants @ Swiss National Science Foundation (SNF)
Oct. - Nov. Service: reviewer @ ICLR-2018
Oct. 11 At: Le Cam Data Science Colloquium @ EDF Lab, Paris-Saclay
Oct. 9 Talk @ CREST Statistics Seminar, ENSAE
[abstract, slides]
Oct. Welcome to & start working with Gaspar Massiot (postdoc), Romain Brault (postdoc), Alex Lambert (PhD), Moussab Djerrab (PhD).
Sept. 27 Service: Committee Member @ Data Science Master: internship defense (École Polytechnique - morning, Télécom ParisTech - afternoon)
Sept. 9 Organizing: Our Learning on Distributions, Functions, Graphs and Groups workshop proposal with Florence, Krikamol & Bharath @ NIPS-2017 got accepted.
Sept. 4-5 Service: Committee Member @ Data Science Master: internship defense
Sept. 4 Paper: A Linear-Time Kernel Goodness-of-Fit Test.
to appear @ NIPS-2017 , see 'Dec. 4-9'.
Aug. 28 - Sept. 1 Organizing: I am Program Chairing DS3.
Aug 28 TR: Characteristic and Universal Tensor Product Kernels
[arXiv: paper, HAL: paper]
Aug 9 Paper @ ICML-2017
[slides, poster; further details @ 'May 12']
July 27 Talk @ Summer School on Mathematical and Computational Methods for Life Sciences
[slides]
July 14-17 At: Greek Stochastics Workshop - Model Determination; details @ 'Apr. 17'
[slides]
July 3-7 Visitor: Bharath Sriperumbudur is visiting us and gives seminars.
July 3 Service: Ph.D. committee member @ Romain Brault's defense
June - July Service: reviewer @ NIPS-2017
June 28 Workshop: UCL Workshop on the Theory of Big Data
[abstract, slides, code]
June 22-23 Visitor: Barnabás Póczos is visiting us and gives seminars.
June 21 Visitor: Florence d'Alché-Buc & Romain Brault are visiting us and give seminars.
June 19-20 At: Structured Regularization Summer School
June 14 At: Le Cam Data Science Colloquium @ Digiteo LABS
May - June Service: SPC @ UAI-2017
May 22 TR: A Linear-Time Kernel Goodness-of-Fit Test
[arXiv: paper, HAL: paper; code]

= adaptive linear-time nonparametric goodness-of-fit test

May 17 Talk @ Télécom ParisTech: PASADENA Seminar
[abstract, slides, code]
May 12 Paper: An Adaptive Test of Independence with Analytic Kernel Embeddings
[paper, paper (ICML website), preprint on arXiv; code]
accepted @ ICML-2017

= adaptive linear-time nonparametric independence test

May 9 At: ParisBD-2017
May 4 Talk @ ML journal club
[slides]
Apr. - May Service: Area Chair @ ICML-2017
Apr. 24 - Sept. 29 Service: referent professor @ Camille Jandot's internship
Apr. 27 Visitor: Kirthevasan Kandasamy is visiting us & gives a seminar.
Apr. 17-20 At: DALI-2017
[poster]
Apr. 17 Workshop: A Fast Goodness-of-Fit Test with Analytic Kernel Embeddings
[abstract, code]
accepted @ Greek Stochastics Workshop - Model Determination
Apr. 11 Visitor: David Lopez-Paz is visiting us & gives a seminar.
March Service: reviewer @ COLT-2017
Mar. 29 Visitor: Ming Yuan is visiting us & gives a seminar.
Mar. 27 Talk @ Henri Poincaré Institute: Parisian Statistics Seminar
[abstract, slides, code]
Mar. 24 Talk @ Marseilles: Signal Processing and Machine Learning Seminar
[abstract, slides, code]
Mar. 16 Talk @ Orsay: Probability and Statistics Seminar
[abstract, slides, code]
Spring Lecturing: Structured Data: Learning, Prediction, Dependency, Testing
Feb. 27 Grant: a postdoc/1Y @ Labex DigiCosme, joint work with Florence d'Alché-Buc & Arthur Tenenhaus
Feb. 24 Workshop: Probabilistic Graphical Model Workshop
[slides, code]
Feb. 2 Talk @ Télécom ParisTech: Machine Learning Seminar
[abstract, slides, code]
Feb.- Organizing: ML journal club
Jan. 31 Visitor: Lorenzo Rosasco is visiting us & gives a seminar.
- Jan. Service: SPC @ AISTATS-2017
2016
Dec. 3-11 At: NIPS-2016: our 3-minute spotlight video, slides, poster, code; workshop
Nov. 27 - Dec. 2 At: New Directions for Learning with Kernels and Gaussian Processes Dagstuhl Seminar
[slides, code]
Nov. 22 Talk @ CMAP seminar
[abstract, slides, code]
Nov. 21 Talk @ Facebook AI Research
[abstract, slides, code]
Nov. 18 Software: ITE in Python: released.

= several information theoretical estimators

Oct. 18 TR: An Adaptive Test of Independence with Analytic Kernel Embeddings
[paper, code]
Oct. 5 Talk @ 'Statistics with coffee' seminar
[slides]
Fall Lecturing: Functional Data Analysis
Sept. 29 Paper: Learning Theory for Distribution Regression
[paper, code]
appeared @ JMLR

= minimax optimal regression on probability distributions

Sept. Starting at École Polytechnique!
Organizing: Adaptive and Scalable Nonparametric Methods in ML workshop @ NIPS-2016
Aug. 24 Talk @ Realeyes
[slides, code]
Aug. 12 Paper: Interpretable Distribution Features with Maximum Testing Power
[paper, 3-minute spotlight video, poster, code]
to appear @ NIPS-2016 (full oral paper = top 1.84%)

= adaptive linear-time nonparametric two-sample test

July 10 Workshop: Kernel methods for adaptive Monte Carlo
[abstract, slides]
presented @ Greek Stochastics Workshop on Big Data and Big Models

= kernel based fast sampling from Bayesian posteriors (big data regime)

June 29 Workshop: eResearch Domain launch event (London)
[poster]
June 22 Talk @ PRNI-2016
[abstract, slides]
June 11-16 At: ISNPS-2016; details @ 'Mar. 17'
May 6 Workshop: Distinguishing Distributions with Interpretable Features
[paper, spotlight, poster, code]
accepted @ ICML-2016: Data-Efficient ML
Apr. 25 Talk @ UCSD
[slides, code]
Service: SPC @ UAI-2016
Mar. 17 Workshop: Minimax-Optimal Distribution Regression
[abstract, slides, code]
accepted @ ISNPS-2016
Mar. 16 Talk @ MPI, Tübingen: Special Symposium on Intelligent Systems
[abstract, slides, code]
Mar. 14 Talk @ École Polytechnique
[abstract, slides]
Mar. 9 Talk @ Imperial College London
[abstract, slides]

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