Zoltán Szabó

Department of Statistics, LSE [local time]
Houghton Street, London, WC2A 2AE, UK

Email: x [at] y; x = z.szabo (default) or x = dsi.turing.academic (Turing matters), y = lse.ac.uk
Office: COL 5.14 (Columbia House, 5th floor, room #14) [campus map]
Zoltán Szabó
(photo credit: Peter Richtárik)
I am a Professor of Data Science at the Department of Statistics and a member of the Data Science group. My research interest is statistical machine learning with focus on kernel methods, information theoretical estimators (ITE), scalable computation, and their applications. These applications include shape-constrained prediction, hypothesis testing, safety-critical learning, style transfer, distribution regression, dictionary learning, structured sparsity, independent subspace analysis and its extensions, Bayesian inference, finance, economics, analysis of climate data, criminal data analysis, collaborative filtering, emotion recognition, face tracking, remote sensing, natural language processing, and gene analysis.
CV, bio, profile.
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2025
May 6-9 Organizing: Uncertainty in multivariate, non-Euclidean, and functional spaces: theory and practice workshop
@ Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning (Isaac Newton Institute),
with Alessandra Menafoglio, David Ginsbourger, Florence d’Alché-Buc, Judith Rousseau, Neil Lawrence.
Apr. 13-16 At: DALI-2025
Apr. 7-8 Organizing: LSE Statistics Research Showcase
Winter Term (Jan. - Apr.) Lecturing: Graph Data Analytics and Representation Learning
2024
Nov. - Dec. Service: Senior Area Chair @ AISTATS-2025
Sept. 27 Lecturing (mini-course): Linux – The Operating System of Freedom
[abstract, slides]
Sept. 25 Paper: 'The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels' accepted at NeurIPS-2024
[paper, paper (arXiv)]

= minimax rate for HSIC estimation on Rd

Aug. 28 Paper: 'Random Fourier Signature Features' @ SIAM Journal on Mathematics of Data Science
[paper (arXiv), code]

= large-scale, GPU-accelerated signature kernel estimators

June - Aug. Service: Senior Area Chair @ NeurIPS-2024
July 3 TR: To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning
[paper (arXiv)]

= flexible policy switching framework for offline RL, balancing between long-term return maximization and the cost of switching

July 1 TR: Forward and Backward State Abstractions for Off-policy Evaluation
[paper (arXiv), code]

= two-step procedure for dimensionality reduction in off-policy evaluation

June 20-21 Organizing: LSE Statistics Research Showcase
June - Service: DTS Management Board
June 13 TR: Nyström Kernel Stein Discrepancy
[paper (arXiv), code]

= Nyström-based accelerated computation of kernel Stein discrepancy

June 11 Grant: LSE Global Research Fund, joint work with Bharath Sriperumbudur & Florian Kalinke.
Mar. - Apr. Service: AC @ ICML-2024, SPC @ COLT-2024
Mar. 13 TR: The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
[paper, paper (arXiv)]

= minimax rate for HSIC estimation on Rd

Feb. - Apr. Service: AC @ UAI-2024
Feb. 12 Talk @ Computer Science Colloquium, University of Warwick
[abstract, slides]
Winter Term Lecturing: Graph Data Analytics and Representation Learning
Winter Term (Feb. 5) Lecturing: Foundations of Machine Learning: one session on Support Vector Machines
Jan. - Start working with Tao Ma (PhD student)
2023
Dec. 18 Talk @ CMStatistics-2023: Economic Data Analysis and Statistical Inference to Unfold Uncertainty session
[slides]
Dec. 17 Talk @ CMStatistics-2023: Statistical Machine Learning with Kernels and Non-linear Transformations session
[slides]
Dec. 16 Talk @ CMStatistics-2023: Advances in Kernel Methods and Gaussian Processes session
[slides]
Nov. 22 TR: Random Fourier Signature Features
[paper (arXiv), code]

= large-scale, GPU-accelerated signature kernel estimators

Nov. - Dec. Service: Senior Area Chair @ AISTATS-2024
Sept. 22 Lecturing (mini-course): Linux – The Operating System of Freedom
[abstract, slides]
Sept. 21 Paper: 'Kernelized Cumulants: Beyond Kernel Mean Embeddings' @ NeurIPS-2023
[paper (NeurIPS), paper (arXiv), spotlight, poster, code]

= higher-degree generalization of MMD and kernel Lancaster interaction; applications: environmental and traffic data analysis

Sept. - Working with Xiaoyi Wen (visiting PhD student)
Sept. - Service: Turing Academic Liaison
Jun. - Aug. Service: Area Chair @ NeurIPS-2023
June 26-30 Talk @ Lifting Inference with Kernel Embeddings (LIKE23)
[slides]
June 10-14 Plenary Talk @ ACDL-2023
[slides-1, slides-2]
June 5-6 Organizing: LSE Statistics Research Showcase
May 23 Lecturing (mini-course): Linux – The Operating System of Freedom
[slides]
May 8 Paper: 'Nyström M-Hilbert-Schmidt Independence Criterion' @ UAI-2023
[paper, paper (UAI), supplement (UAI), paper (arXiv), code]

= consistent estimation of HSIC for ≥ 2 components using the Nyström technique; applications: weather causal discovery, dependency testing of media annotations

Mar. - Apr. Service: SPC @ COLT-2023, AC @ ICML-2023, UAI-2023
Mar. 2- Service: Senior Associate Editor @ ACM Transactions on Probabilistic Machine Learning
Feb. 20 TR: Nyström M-Hilbert-Schmidt Independence Criterion
[paper (arXiv), code]

= consistent estimation of HSIC for ≥ 2 components using the Nyström technique

Jan. 31 TR: Kernelized Cumulants: Beyond Kernel Mean Embeddings
[paper (arXiv), code]

= higher-degree generalization of MMD and HSIC; applications: environmental and traffic data analysis

Lent Term Lecturing: Graph Data Analytics and Representation Learning
2022
Nov. - Dec. Service: Senior Area Chair @ AISTATS-2023
Oct. 26 Paper: @ JMLR
[paper, paper (JMLR), code]

= hard affine SDP shape constraints in vector-valued RKHSs; applications: shape optimization, safety-critical control, robotics, econometrics

Oct. - Dec. Service: Area Chair @ ICLR-2023
Michaelmas Term Lecturing: Artificial Intelligence
Sept. 29 Talk @ CGO Seminar
[abstract, slides]
Sept. 21 Talk @ Data Science Research Lightning Talks event (DSI)
[slides]
Sept. 8 Talk @ BIRS workshop on New Interfaces of Stochastic Analysis and Rough Paths
[abstract, slides]
Sept. - Dec. Working with Florian Kalinke (visiting PhD student)
Sept.- Service: Programme Director of MSc Data Science
Sept. Welcome to & start working with Sakina Hansen (PhD student ) and Pingfan Su (PhD student)
July 28 TR: Handling Hard Affine SDP Shape Constraints in RKHSs
(extended and simpler proof, new application in robotics)
[paper (arXiv)]

= hard affine SDP shape constraints in vector-valued RKHSs; applications: shape optimization, robotics, econometrics

Jun. - Aug. Service: Area Chair @ NeurIPS-2022
June 23 Paper: 'Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'' @ Biometrika
[paper (arXiv), paper (Biometrika)]
June 14-15 Organizing: LSE Statistics Research Showcase
June 4 Talk @ EcoSta
[abstract, slides]
May 26 Talk @ DataSig Seminar
[abstract, slides]
May 14 Paper: 'Functional Output Regression with Infimal Convolution: Exploring the Huber and Epsilon-Insensitive Losses' accepted @ ICML-2022
[paper, paper (arXiv), code, presentation (video)]

= functional output regression with sparsity-inducing and robust loss; applications: neuroimaging, speech analysis

Apr. - May Service: Area Chair @ UAI-2022
Feb. - Apr. Service: Area Chair @ ICML-2022
Mar. - Apr. Service: SPC @ COLT-2022
Jan. 26 Talk @ Gatsby Unit
[abstract, slides]
Jan. 12 Talk @ Lifting Inference with Kernel Embeddings (LIKE22) winter school and workshop
[slides]
2021
Dec. 19 Talk @ CMStatistics-2021
[abstract, slides]
Dec. 13 Talk @ Data Science Institute
[slides]
Nov. - Dec. Service: Area Chair @ AISTATS-2022
Oct. - Dec. Service: Area Chair @ ICLR-2022
Nov. 4 Talk @ Data Science Seminar at Eurecom
[abstract, slides]
Oct. 20 TR: Kernel Minimum Divergence Portfolios
[paper (arXiv)]

= analytic mean embeddings, better concentration of the resulting MMD estimators, portfolio optimization

Oct. 14 Talk @ LSE Statistics Open House
[slides-1, slides-2]
Oct.- Organizing: Data Science Seminars (local-, departmental archive)
Sept.- Service: DSI Management Committee
Michaelmas Term Lecturing: Artificial Intelligence
Sept. Starting at LSE!
June - Sept. Service: Area Chair @ NeurIPS-2021
Aug. 31 At: Linda's PhD defense
July 8 Talk @ EUROPT-2021
[slides]
July 7 At: Alex's PhD defense
July 5 At: Pierre-Cyril's PhD defense
May 20 (morning) Talk @ LIS, Aix-Marseille University
[abstract, slides]
May 18-21 At: RESIM-2021
Mar. 24 Lecturing: Mini-Course on Independence Measures and Testing
Mar. - Apr. Service: Area Chair @ ICML-2021
Mar. - Apr. Service: SPC @ UAI-2021 & COLT-2021
Service: reviewing grants @ Israel Science Foundation (ISF)
Mar. 4 Talk @ Department of Statistics, LSE
Feb. - Mar. Service: Area Chair @ IJCAI-2021
Feb. 12 Talk @ Texas A&M University: Department of Statistics
[abstract, slides]
Feb. 4 TR: Emotion Transfer Using Vector-Valued Infinite Task Learning
[paper, paper (arXiv), demo (video), code]

= emotion/style transfer with operator-valued kernels

Jan. 6-8 Organizing: I am Program Chairing DS3-2020 (postponed to Jan 2021 due to COVID-19)
Jan. 6 TR: Handling Hard Affine SDP Shape Constraints in RKHSs
[paper (arXiv), paper (HAL)]

= hard affine SDP shape constraints in vector-valued RKHSs; applications: shape optimization, safety-critical control, econometrics

2020
Dec. 15 Talk @ Meeting on Mathematical Statistics (MMS)
[abstract, slides]
Dec. 6-12 At: NeurIPS-2020
[paper, slides, spotlight video, code]
Nov. - Dec. Service: Area Chair @ AISTATS-2021
Nov. 30 - Dec. 1 Organizing: StressTest-2020 workshop with Stefano De Marco and Emmanuel Gobet
Oct. - Nov. Service: Area Chair @ ICLR-2021
Sept. 25 Paper: 'Hard Shape-Constrained Kernel Machines' accepted @ NeurIPS-2020
[paper, paper (NeurIPS), paper (arXiv), paper (HAL), slides, code]

= SVMs with hard affine shape constraints on derivatives; applications: joint quantile regression, economics, analysis of aircraft trajectories

Sept.- Organizing: I have joined to the organizing board of the Statistics Seminars (CREST-CMAP).
Sept. 7 Talk @ SMAI-MODE (Pierre-Cyril)
[abstract, slides]
June - Aug. Service: Area Chair @ NeurIPS-2020
July 27-31 Poster: 'Hard Shape-Constrained Kernel Regression' @ SPIGL
[abstract, poster]
July 11-17 Talk @ IFAC-WC (Pierre-Cyril)
[slides, talk (video)]
July 14-15 Service: Newcomers' Mentoring @ ICML-2020
June- Service: Editorial Board Member @ JMLR
June 11 Paper: Orlicz Random Fourier Features @ JMLR
[paper, JMLR: paper, HAL: paper]

= tight uniform guarantees for random Fourier features under mild conditions for arbitrary order derivatives

May 27 TR: Hard Shape-Constrained Kernel Machines
[arXiv: paper; HAL: paper]

= SVMs with hard affine shape constraints on derivatives

May 18 - Aug. 18 Welcome to & start working with Bechir Trabelsi (M.Sc. intern).
Apr. 23- Organizing: SIMPAS Group Meeting
Apr. 6 Grant: Europlace Institute of Finance (EIF), joint work with Dino Sejdinovic & Olivier Derollez.
Apr. 1 Service: Recruitment Committee Member (M.Sc. @ Data Science for Business)
Mar. - Apr. Service: SPC @ UAI-2020
Feb. - Apr. Service: Area Chair @ ICML-2020
Feb. - Mar. Service: SPC @ IJCAI-2020
Mar. 5 Talk @ ML journal club
[slides]
Feb. 27 Paper: 'Kernel Regression for Vehicle Trajectory Reconstruction under Speed and Inter-vehicular Distance Constraints' accepted @ IFAC World Congress (IFAC WC)
[paper]

= application of shape-constrained SVMs for trajectory reconstruction from noisy GPS measurements

Service: External reviewer of Luigi Carratino's PhD thesis
Jan. 30 Paper: 'Kernel Regression with Hard Shape Constraints' accepted @ SMAI-MODE 2020
[slides]
Jan 21 Talk @ D. E. Shaw Group
Jan 9-11 Talk @ International Conference on Modern Mathematical Methods and High Performance Computing in Science & Technology (M3HPCST-2020)
[abstract, slides]
2019
Dec. 29- Service: Associate Editor @ Mathematical Foundations of Computing
Dec. 26-30 Talk @ International Indian Statistical Association Conference (IISA-2019)
[abstract, slides]
Dec 20 TR: Orlicz Random Fourier Features
[paper; HAL: paper]

= fast uniform approximation of arbitrary-order kernel derivatives with random Fourier features under mild (alpha-exponential Orlicz spectrum) assumption

Dec. 16 HDR with distinction.
Nov. 19 At: Workshop on Regularisation for Inverse Problems and Machine Learning
Nov. - Dec. Service: Area Chair @ AISTATS-2020
Sept. 23 Talk @ EPFL
[abstract, slides]
Sept. 2-5 At: DALI-2019 (San Sebastian, Spain)
[poster]
July - Aug. Service: Senior Area Chair @ NeurIPS-2019
July 15-17 Lecturing @ Summer School on Data Science for Document Analysis and Understanding
[slides]
July 11-13 Talk @ Gatsby 21st Birthday Symposium
[slides]
June 24-28 Organizing: I am Program Chairing DS3-2019.
June 19-23 Talk @ Third International Conference on Mathematics of Data Science (MathoDS 3)
[abstract, slides, code]
June 9-15 At: ICML-2019
[paper, slides, poster, code]
May 30- Service: arXiv Statistics Advisory Committee Member
May 29 Paper: 'A Functional Extension of Multi-Output Learning' will be presented @ AMTL-2019
[paper]
May 28-29 At: Stress Test Workshop
May 27 Service: Recruitment Committee Member (M.Sc. @ Data Science for Business)
May 17 Paper: 'Infinite Task Learning in RKHSs' will be presented @ CAp-2019
May 7 & 10 Lecturing: Data for Management Certificate @ HEC Paris
Apr. 22 Paper: 'MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means' accepted @ ICML-2019
[preprint on arXiv: paper]

= new MMD and mean embedding estimators: with optimal sub-Gaussian rate & excessive resistance to contamination

Apr. 16-18 At: AISTATS-2019
[paper-1, poster-1, paper-2, poster-2]
Apr. 15 Talk @ RIKEN AIP workshop
[abstract, slides, code]
Apr. Service: reviewer @ SPARS-2019 & ICANN-2019
Mar. 25 - July 31 Service: referent professor @ Nicolas Bonnet's internship, Atos India.
Spring Lecturing: Advanced Machine Learning
Spring Lecturing: Structured Data: Learning, Prediction, Dependency, Testing
Mar. - Apr. Service: SPC @ IJCAI-2019
Mar. 7 Visitor: Guillaume Perrin is visiting us and gives an external seminar.
Feb. - Mar. Service: Area Chair @ ICML-2019
Feb. 15 At: PASADENA workshop
[slides]
Feb. 12 - Mar. 12 Service: For one month I also serve (in addition to stat.ML) as the cs.LG moderator on arXiv, replacing Thomas Dietterich.
Feb. 8 Service: Recruitment Committee Member (M.Sc. @ Data Science for Business)
Feb. 7 Visitor: Anne Sabourin is visiting us and gives an external seminar.
Jan. - Feb. Service: Program Committee Member @ ICML-2019 workshops
Jan. 3-5 At: DALI-2019
[poster]
2018
Dec. 23 Paper: 2 papers accepted @ AISTATS-2019
[preprints on arXiv: paper-1, paper-2]
Dec. 19 I am being interviewed to the film Mind (topic: AI & Brain).
Dec. 2-8 At: NeurIPS-2018
Nov. 29, Dec. 13; Jan. 10 (2019) Visitor: Stéphane Girard is visiting us and gives a mini course.
Nov. 21 Service: Recruitment Committee Member (M.Sc. @ Data Science for Business)
Oct. - Dec. Service: SPC @ AISTATS-2019
Fall Lecturing: Statistics
Fall Lecturing: Introduction to Machine Learning
Oct. 31 Visitor: Lilian Besson is visiting us and gives a seminar.
Oct. 26 Talk @ Statistical Seminar in Rennes
[abstract, slides]
Oct. 11 TR: On Kernel Derivative Approximation with Random Fourier Features
[arXiv: paper, HAL: paper]

= random Fourier features can be as efficient for kernel derivative approximation as for kernel values; tool: unbounded empirical processes

Oct. Service: reviewer @ CiML-2018
Sept. - Oct. Service: reviewer @ ICLR-2019
Sept. 17-20 At: Polish-Italian Mathematical Conference: Challenges and Methods of Modern Statistics
[abstract, slides]
Sept. 10 Service: Committee Member @ X-HEC (M.Sc.): internship defense
Sept. 4 Talk @ B.Sc. Day at CMAP
[slides]
Aug. 30 Paper: Infinite-Task Learning: accepted @ JDSE-2018
[paper, poster, slides]
Aug. 28- Service: I am the moderator of statistical machine learning (stat.ML) on arXiv.
Aug. 2 Paper: Characteristic and Universal Tensor Product Kernels @ JMLR
[paper]

= characterization of when HSIC is an independence measure

Service: reviewing grants @ European Research Council (ERC)
June - Aug. Service: Area Chair @ NIPS-2018
July 18 & 20 Lecturing @ Summer School on Data Science for Document Analysis and Understanding
[slides]
July 10-15 At: ICML-2018
June 25 Paper: Characteristic and Universal Tensor Product Kernels (accepted to JMLR).
[paper; arXiv: paper, HAL: paper; +details @ Aug. 2]
June 25-29 Organizing: I am Program Chairing DS3-2018.
June 19 Visitor: Subhadeep Mukhopadhyay is visiting us and gives a seminar.
June Service: reviewer @ Cambridge University Press
June 11-15 At: Conference of the International Society for Non-Parametric Statistics (ISNPS)
[abstract-1, slides-1; abstract-2, slides-2]
June 5 Visitor: Martin Wainwright is visiting us and gives a seminar.
May 28 - June 1 At: JdS-2018
[slides]
May. 24 TR: Infinite-task learning with vector-valued RKHSs
[arXiv: paper, HAL: paper; code]

= learning continuum number of tasks jointly; examples: cost-sensitive classification, joint quantile regression, density level set estimation; tool: operator-valued kernels.

May 17-20 At: Hangzhou International Conference on Frontiers of Data Science
[abstract, slides]
Apr. 10 Visitor: Claire Monteleoni is visiting us and gives a seminar.
Apr. 3-5 At: DALI-2018
[poster]
Apr. Welcome to & start working with Meyer Scetbon (M.Sc. intern).
March Service: reviewer @ COLT-2018
Mar. 23 Talk @ Sertis (Wittawat)
[slides]
Mar. 22 Service: Committee Member @ M.Sc. defense: Statistical Models in Biology and Physics.
Mar. 13-14 At: PGMO lecture series by Sébastien Bubeck on Bandit Convex Optimization
Mar. 6 Visitor: Alessandro Rudi is visiting us and gives a seminar.
Mar.- Member of the French Statistical Society (SFdS)
Feb. - Mar. Service: Area Chair @ ICML-2018
Feb. 28 Talk @ Laboratory for Information and Inference Systems, EPFL
[abstract, slides]
Feb. 26 Talk @ Machine Learning & Computational Biology Lab, D-BSSE, ETH Zürich
[slides]
Feb. 20 Workshop: Workshop on Functional Inference and Machine Intelligence
[slides, code]
Feb. 14 TR: MONK – Outlier-Robust Mean Embedding Estimation by Median-of-Means
[arXiv: paper, HAL: paper]

= consistent outlier-robust mean embedding & MMD estimation, with optimal rates.

Feb. 13 Talk @ Tao Seminar, INRIA Saclay
[abstract, slides]
Feb. 2 At: CentraleSupélec & ICM workshop
Jan. 24 Interview at TWiML & AI (YouTube, SoundCloud) on our NIPS-2017 Best Paper Award is up.
Jan. 23 Visitor: Marco Cuturi is visiting us and gives a seminar.
Jan. Welcome to & start working with Linda Chamakh (PhD).
Spring Lecturing: Structured Data: Learning, Prediction, Dependency, Testing
Spring Lecturing: Advanced Machine Learning
2017
Dec. 11-15 Visit @ Department of Statistics, Pennsylvania State University. Talk: slides
Dec. 4-9 At: NIPS-2017.
Our submission got one of the 3 Best Paper Awards! (out of 3240 submissions)
[paper, paper (NIPS website), poster, slides, talk (video), slides @ MLTrain workshop, interview at TWiML & AI (YouTube, SoundCloud), 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, slides]
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, slides]
Nov. 28 Talk @ Yahoo Research, New York
[abstract, slides]
Nov. 27 Guest Lecturing @ Machine Learning Department, Carnegie Mellon University.
[slides, course]
Nov. 14 At: Random matrix advances in large dimensional statistics and machine learning day
Nov. 3 Talk @ Research Seminar, SfS, ETH Zürich
[abstract, slides]
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 student), Moussab Djerrab (PhD student).
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 DS3-2017.
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 Lecturing @ 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: PhD 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 @ Machine learning 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: 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: Machine learning 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, Dagstuhl report]
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]