Machine-learning-nederland January 2023

machine-learning-nederland@list.uva.nl

3 participants
4 discussions

February 14–15: Machine Learning Theory Boot Camp
by Wouter Koolen-Wijkstra 31 Jan '23

31 Jan '23

Dear young researchers, Centrum Wiskunde & Informatica (CWI) kindly invites you to a Boot Camp on Machine Learning Theory on February 14 and 15. This event is primarily targeted at PhD students working on theoretical aspects of Machine Learning. It includes 8 tutorials by researchers, two keynote lectures, a poster session and a group dinner. All in all, the Boot Camp will provide a whirlwind tour of Machine Learning Theory in a friendly atmosphere with plenty of interaction. If you like to join, please register here. <https://www.cwi.nl/en/events/cwi-research-semester-programs/machine-learnin…> The tutorials are on these subjects: //Statistical Theory for Deep Learning —// Johannes Schmidt-Hieber// ////Reinforcement Learning////—// Frans Oliehoek// ////Time Series////—// Jaron Sanders/ Explainability///—// Tim van Erven/ ///Statistical Testing in High Dimensions////—// Rui Castro// ////Equivariant/Geometric Deep Learning////—// Gabriele Cesa// /Quantum Learning Theory///—// Ronald de Wolf/ //Learning and Games///—// Matthias Staudigl/ / And the two keynote lectures, which are open to all: /Foundations of Machine Learning Systems///—// Bob Williamson, University of Tübingen/ A Tale of Two Non-parametric Bandit Problems///—// Emilie Kaufmann, CNRS, Univ. Lille For more details, see the website <https://www.cwi.nl/~wmkoolen/MLT_Sem23/bootcamp.html>. The event takes place in the CWI building, Science Park 123, Amsterdam. The Boot Camp is part of the Machine Learning Theory Semester Programme <https://www.cwi.nl/~wmkoolen/MLT_Sem23/index.html>, which runs in Spring 2023. Best regards on behalf of CWI from the program committee, Wouter Koolen

1 0

February 14 Afternoon: Lectures on Machine Learning Theory by Williamson and Kaufmann
by Wouter Koolen-Wijkstra 18 Jan '23

18 Jan '23

Dear colleagues, Centrum Wiskunde & Informatica (CWI) kindly invites you to a lecture afternoon focused on theory of machine learning, with two distinguished speakers that will illuminate philosophical and technical aspects of machine learning. The program on 14 February: 14.00 Bob Williamson <https://fm.ls/> (University of Tübingen): /Foundations of Machine Learning Systems/ 15.00 Break 15.30 Emilie Kaufmann <https://emiliekaufmann.github.io/> (CNRS, Univ. Lille): /A Tale of Two Non-parametric Bandit Problems/ 16.30 Reception For further details, please check the abstracts below and the website <https://www.cwi.nl/en/events/cwi-research-semester-programs/launch-lecture-…>. These research-level lectures are intended for a non-specialist audience, and are freely accessible. Please register here <https://www.cwi.nl/en/events/cwi-research-semester-programs/launch-lecture-…>. The event takes place in the Amsterdam Science Park Congress Centre. This "Launch Lecture", kicks off the Machine Learning Theory Semester Programme <https://www.cwi.nl/~wmkoolen/MLT_Sem23/index.html>, which runs in Spring 2023. Young researchers (PhD students, ....) in Machine Learning Theory may also be interested in the encompassing Boot Camp <https://www.cwi.nl/~wmkoolen/MLT_Sem23/bootcamp.html> on 14 and 15 February. Best regards on behalf of CWI from the program committee, Wouter Koolen Bob Williamson /Foundations of Machine Learning Systems/ *Abstract:* I will present some new insights into some foundational assumptions about machine learning systems, including why we might want to replace the expectation in our definition of generalisation error, why independence is intrinsically relative and how it is intimately related to fairness, why the data we ingest might not even have a probability distribution, and what one might do in such cases, and how we have been (perhaps unwittingly) working with these more exotic notions for some time already. Emilie Kaufmann /A Tale of Two Non-parametric Bandit Problems/ *Abstract:* In a bandit model an agent sequentially collects samples (rewards) from different distributions, called arms, in order to achieve some objective related to learning or playing the best arms. Depending on the application, different assumptions can be made on these distributions, from Bernoulli (e.g., to model success/failure of a treatment) to complex multi-modal distributions (e.g. to model the yield of a crop in agriculture). In this talk, we will present non-parametric algorithms which adapt optimally to the actual distributions of the arms, assuming that they are bounded. We will first show the robustness of a Non Parametric Thompson Sampling strategy to a risk-averse performance metric. Then, we will discuss how the algorithm can be modified to tackle pure exploration objectives, bringing new insights on so-called Top Two algorithms.

1 0

[jobs] Postdoc Position in Causal Inference (University of Amsterdam)
by Joris Mooij 16 Jan '23

16 Jan '23

Vacancy: Postdoctoral Researcher in Causal Inference (University of Amsterdam) ============================================================================== We are looking for an enthousiastic postdoc who enjoys working on statistical problems in causal inference and domain adaptation. We have a vacancy in the recently established Mercury Machine Learning Lab (MMLL). In this lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology (TU Delft) will be working together with data scientists from Booking.com to develop the statistical and machine learning foundations for a new generation of recommendation systems. Motivated by real-world problems faced in industry that involve domain adaptation and optimization, we will investigate fundamental scientific problems regarding generalization and bias removal from a causal perspective. The successful candidate will be based in the Korteweg-De Vries Institute for Mathematics of the University of Amsterdam, the Netherlands, under supervision of prof. dr. Joris Mooij. Application closing date: January 31, 2023 Preferred starting date: ASAP Duration: 3-4 years For further information, including how to apply, see the official job advertisement at: https://vacatures.uva.nl/UvA/job/Postdoctoral-Researcher-in-Causal-Inferenc… ------------------------------------------------------------- Joris Mooij Professor in Mathematical Statistics University of Amsterdam http://www.jorismooij.nl/

1 0

Postdoc position at VU
by Tim van Erven 13 Jan '23

13 Jan '23

Forwarding on behalf of Frank van der Meulen: -------- Forwarded Message -------- Subject: Mailing list Date: Fri, 13 Jan 2023 11:52:57 +0000 From: Meulen, F.H. van der (Frank) <f.h.van.der.meulen(a)vu.nl> To: Tim van Erven <tim(a)timvanerven.nl> ——— The *Department of Mathematics of Vrije Universiteit Amsterdam* welcomes applications for a *three-year Postdoctoral position in Statistics* with emphasis on statistical inference for stochastic processes, graphical models, computational statistics and Bayesian computation. Good programming skills and being able to connect to existing research strengths in the department are assets. The preferred starting date is 1 September 2023 or earlier. Full information on the position can be found at https://workingat.vu.nl/ad/postdoctoral-position-in-statistics/riljhf <https://urldefense.com/v3/__https://workingat.vu.nl/ad/postdoctoral-positio…>

1 0

2024

2023

2022

2021

2020

Machine-learning-nederland January 2023