- Machine-learning-nederland

[CFP] UAI 2023 - deadline Feb 17 23:59 AoE
by Sara Magliacane 15 Feb '23

15 Feb '23

The Conference on Uncertainty in Artificial Intelligence (UAI <https://auai.org/>) is one of the premier international conferences on research related to learning and reasoning in the presence of uncertainty. The conference has been held every year since 1985. The upcoming 39th edition (UAI 2023 <https://auai.org/uai2023/>) will be an in-person conference with virtual elements taking place in Pittsburgh, Pennsylvania, USA from 31 July to 4 August 2023. We invite papers that describe novel theory, methodology and applications related to artificial intelligence, machine learning and statistics. Papers will be assessed in a rigorous double-blind peer-review process, based on the criteria of technical correctness, novelty, clarity of writing, and potential impact. Authors are strongly encouraged to make code and data available. All accepted papers will be presented in poster sessions and spotlight presentations (physically or remotely). Selected papers will have longer presentations and an assigned discussant to foster debate. All accepted papers will be published in a volume of Proceedings of Machine Learning Research (PMLR) <http://proceedings.mlr.press/>. Deadlines and other relevant dates can be found under important dates <https://auai.org/uai2023/dates>. Important dates for authors: - 17 February 2023 (23:59 Anywhere on Earth, AoE): Paper submission deadline - 11-20 April 2023: Author response and discussion period - 8 May 2023: Author notification Papers should be submitted on OpenReview at https://openreview.net/group?id=auai.org/UAI/2023/Conference. Please see Submission Instructions <https://auai.org/uai2023/submission_instructions> for more details on how your manuscript should be formatted. We are looking forward to building an exciting program and we aim to make the most of the advantages that a hybrid conference can create. If you have any particular positive or negative experiences that you would like to share with us, please do not hesitate to email us. If you are interested in giving a tutorial or organising a workshop at UAI 2023, please contact the tutorial chairs (uai2023chairs+tutorials(a)gmail.com) or workshop chairs (uai2023chairs+workshop(a)gmail.com) by 10 March 2023. Relevant dates: - 31 July: Tutorials - 1-3 August: Main conference - 4 August: Workshops Robin Evans and Ilya Shpitser UAI 2023 Program Chairs uai2023programchairs(a)gmail.com

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Third Conference of Young Applied Mathematicians (YAMC)
by YAMC Conference of Young Applied Mathematicians 14 Feb '23

14 Feb '23

Dear all, We are writing to you to promote the third "*Conference of Young Applied Mathematicians*", which will be held between the 18th and the 22nd of September 2023 in Siena (SI), Italy. The conference aims to gather young researchers (PhD students and postdocs) working in the fields of Numerical Analysis, Numerical Modelling, Statistics, and Machine Learning. Every conference day will be opened by a plenary talk that will be given by senior lecturers. The remainder of the session is dedicated to the participants, who are strongly encouraged to present their research projects or activities. To allow everyone to talk, two parallel sessions will be organized, one devoted to Numerical Analysis and Numerical Modelling and one devoted to Machine Learning and Statistics. For further details, please visit the website: http://www.yamc.it/ For anyone interested in taking part in this conference, this is the link for the application form (deadline: 13th May): https://forms.gle/pC1dhvyHo6EG1Kir5 Please keep in mind that we have limited spots. It will still be possible to apply after the early registration closes (with an increased fee) until all the spots are filled. We kindly ask you to help us publicize the event. For any questions, please contact us at info(a)yamc.it Thank you. Best regards, The organizers G. Auricchio, G. A. D'Inverno, C. Graziani, V. Lachi, F. Locatelli, G. Loli, L. Zambon

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PhD position at VU Amsterdam
by Heide, R. de (Rianne) 09 Feb '23

09 Feb '23

I have an opening for a fully funded 4-year PhD position in mathematical statistics. The aim of the project is to develop theory for hypothesis testing with e-values, in particular for multiple testing, while it is also possible to work on related topics such as Bayesian learning and bandits. For more information, see: https://workingat.vu.nl/ad/phd-position-in-mathematical-statistics/dx6kg8 Please help me spread the word! The deadline is April 1st. Best regards, Rianne de Heide (r.de.heide(a)vu.nl)

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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

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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.

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[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/

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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…>

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Postdoc on Machine Learning for Heterogeneous Causal Effect Estimation
by Krijthe, J.H. (Jesse) 23 Dec '22

23 Dec '22

We have an open postdoc position to come work with us on machine learning models for heterogeneous effect estimation with applications in cognitive health at Radboud University in Nijmegen, The Netherlands. If you are interested in the intersection of machine learning and causal inference please consider applying (deadline: 8 January 2023). More details and link to the application form here: https://www.ru.nl/en/working-at/job-opportunities/postdoctoral-researcher-f…

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Fwd: Posting a vacancy
by Tim van Erven 19 Dec '22

19 Dec '22

-------- Forwarded Message -------- Subject: Posting a vacancy Date: Mon, 19 Dec 2022 10:11:33 +0000 From: Wiel, M.A. van de (Mark) <mark.vdwiel(a)amsterdamumc.nl> To: machine-learning-nederland-owner(a)list.uva.nl <machine-learning-nederland-owner(a)list.uva.nl> Hi, can I post this vacancy on the ml_ned mailing list? Cheers, Mark van de Wiel Postdoc vacancy at the department of epidemiology and data science, Amsterdam UMC Interested in improving tree-based learners in a medical context? Please check out this vacancy: https://werkenbij.amsterdamumc.org/en/vacatures/research/postdoc-position-o… ______________________________________________________ AmsterdamUMC disclaimer : www.amsterdamumc.org/disclaimers

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Reminder: Thematic seminar: Umut Simsekli today at 16h00
by Tim van Erven 14 Nov '22

14 Nov '22

Dear all, Heads up: Umut Şimşekli's in person talk at the UvA is today: *Umut Şimşekli* (INRIA/École Normale Supérieure, https://www.di.ens.fr/~simsekli/) *Monday November 14*, 16h00-17h00 In person, at the University of Amsterdam Location: Science Park 904, Room A1.24 *Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms* Understanding generalization in deep learning has been one of the major challenges in statistical learning theory over the last decade. While recent work has illustrated that the dataset and the training algorithm must be taken into account in order to obtain meaningful generalization bounds, it is still theoretically not clear which properties of the data and the algorithm determine the generalization performance. In this talk, I will approach this problem from a dynamical systems theory perspective and represent stochastic optimization algorithms as random iterated function systems (IFS). Well studied in the dynamical systems literature, under mild assumptions, such IFSs can be shown to be ergodic with an invariant measure that is often supported on sets with a fractal structure. We will prove that the generalization error of a stochastic optimization algorithm can be bounded based on the ‘complexity’ of the fractal structure that underlies its invariant measure. Leveraging results from dynamical systems theory, we will show that the generalization error can be explicitly linked to the choice of the algorithm (e.g., stochastic gradient descent – SGD), algorithm hyperparameters (e.g., step-size, batch-size), and the geometry of the problem (e.g., Hessian of the loss). We will further specialize our results to specific problems (e.g., linear/logistic regression, one hidden-layered neural networks) and algorithms (e.g., SGD and preconditioned variants), and obtain analytical estimates for our bound. For modern neural networks, we will develop an efficient algorithm to compute the developed bound and support our theory with various experiments on neural networks. The talk is based on the following publication: Camuto, A., Deligiannidis, G., Erdogdu, M. A., Gurbuzbalaban, M., Simsekli, U., & Zhu, L. (2021). Fractal structure and generalization properties of stochastic optimization algorithms. Advances in Neural Information Processing Systems, 34, 18774-18788. Seminar organizers: Tim van Erven Botond Szabo https://mschauer.github.io/StructuresSeminar/ -- Tim van Erven<tim(a)timvanerven.nl> www.timvanerven.nl

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Machine-learning-nederland