Machine-learning-nederland December 2024

machine-learning-nederland@list.uva.nl

5 participants
5 discussions

Annual meeting of the EMS TAG SciML, MOX, Department of Mathematiks, Politecnico di Milano, Italy, March 24-26, 2025
by Alexander Heinlein 21 Dec '24

21 Dec '24

Dear Colleagues, We are delighted to invite you to the Annual Meeting of the Topical Activity Group for Scientific Machine Learning of the European Mathematical Society (EMS TAG SciML) which will take place at MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy from March 24-26, 2025; see https://www.mate.polimi.it/events/EMS-TAG-SciML-25/index.php The meeting will bring together researchers and practitioners from across Europe and beyond to discuss the latest developments and future directions in the field of scientific machine learning. The program includes the following features * Invited Lectures by leading experts in the field from academia and industry * Tutorial Session introducing to topics of scientific machine learning * Poster Presentations showcasing cutting-edge research * Panel Discussion/Round Table on emerging trends and challenges * Networking Opportunities to connect with peers and collaborators We encourage contributions to the poster session from all areas of scientific machine learning; the deadline for poster submissions is January 10, 2025. Participation in the workshop is free of charge but registration is required (https://www.mate.polimi.it/events/EMS-TAG-SciML-25/index.php) We look forward to welcoming you to Milan. Best regards, On behalf of the EMS TAG SciML Organizing and Scientific Committees Axel Klawonn (spokesperson of the EMS TAG SciML) P.S.: For more information on the EMS TAG SciML, see https://ems-tag-sciml.github.io/ -- --- Alexander Heinlein Delft University of Technology (TU Delft) Delft Institute of Applied Mathematics (DIAM) Mekelweg 4, 2628CD Delft, Netherlands https://searhein.github.io

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BeNeRL Reinforcement Learning Seminar: Hojoon Lee from KAIST AI (December 19, this Thursday)
by Yang, Z. (Zhao) 16 Dec '24

16 Dec '24

Dear colleagues, Our next BeNeRL Reinforcement Learning Seminar (Dec. 19) is coming: Speaker: Hojoon Lee (https://joonleesky.github.io<https://joonleesky.github.io/>), PhD student from KAIST AI. Title: Designing Neural Network Architecture for Deep Reinforcement Learning Date: December 19, 16.00-17.00 (CET) Please find full details about the talk below this email and on the website of the seminar series:https://www.benerl.org/seminar-series The goal of the online BeNeRL seminar series is to invite RL researchers (mostly advanced PhD or early postgraduate) to share their work. In addition, we invite the speakers to briefly share their experience with large-scale deep RL experiments, and their style/approach to get these to work. We would be very glad if you forward this invitation within your group and to other colleagues that would be interested (also outside the BeNeRL region). Hope to see you on December 19! Kind regards, Zhao Yang & Thomas Moerland VU Amsterdam & Leiden University —————————————————————— Upcoming talk: Date: December 19, 16.00-17.00 (CET) Speaker: Hojoon Lee (https://joonleesky.github.io<https://joonleesky.github.io/>) Title: Designing Neural Network Architecture for Deep Reinforcement Learning Zoom: https://universiteitleiden.zoom.us/j/65411016557?pwd=MzlqcVhzVzUyZlJKTEE0Nk… Abstract: While scaling laws have accelerated breakthroughs in computer vision and language modeling, their effects are less predictable in reinforcement learning (RL), where simply “scaling up” data, parameters, and computations rarely guarantees better results. In this talk, I will explore the barriers that make scaling challenging in RL and introduce new architectural designs that alleviate these challenges. I will also discuss future research opportunities that can improve scaling laws in RL. Bio: Hojoon is a Ph.D. student at KAIST AI, advised by Professor Jaegul Choo. He previously received his M.S. from KAIST and his B.S. from Korea University. In 2024, he interned with the GranTurismo team at Sony AI, mentored by Takuma Seno and Professor Peter Stone. In 2022, he interned with the RL team at Kakao, developing a new RL framework Jordly. His research focuses on designing network architectures and algorithms for RL that can continually learn, adapt, and generalize in dynamic environments.

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Spring School on Control Theory and Reinforcement Learning, 17-21 March 2025, at CWI, Amsterdam, NL
by Aditya Gilra 13 Dec '24

13 Dec '24

Dear all, We invite registrations for the Spring School on Control Theory and Reinforcement Learning from 17-21 March 2025, at the Centrum Wiskunde & Informatica (CWI, research institute for mathematics and computer science in the Netherlands). This spring school emphasizes connections across control theory, reinforcement learning and stochastic approximation, enabling students to access these broader themes and start to work on cross-cutting projects. The school will be at a preparatory PhD level, suitable for advanced Master's and starting PhD students in these areas. The Spring School is part of a Research Semester Programme at CWI, with further workshops planned in 2025. Please see the full programme here: https://www.cwi.nl/en/events/cwi-research-semester-programs/control-theory-… Please apply for the spring school here: https://www.cwi.nl/en/events/cwi-research-semester-programs/spring-school-c… If you have any questions about our Spring School, please contact events(a)cwi.nl . We look forward to seeing you. On behalf of the organizers of the Research Semester Programme: Control Theory and Reinforcement Learning: Connections & Challenges.

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Fwd: RL Workshop in Mannheim
by Tim van Erven 13 Dec '24

13 Dec '24

Forwarding on behalf of Mathias Staudigl (note the excellent line-up of speakers): -------- Forwarded Message -------- Subject: RL Workshop in Mannheim Date: Tue, 10 Dec 2024 07:24:20 +0100 From: Mathias Staudigl <mathias.staudigl(a)uni-mannheim.de> To: Tim van Erven <tim(a)timvanerven.nl> Dear colleagues, On Feb 7th 2025 we are running a workshop on reinforcement learning (sample based stochastic control) at the University of Mannheim. Speakers are • Peter Dayan (MPI Tübingen) • Daniele Calandriello (Deepmind) • Kassraie Parnian (ETH Zürich) • Alberto Maria Metelli (Politecnico di Milano) • Michael Muehlebach (MPI Tübingen) • Gergely Neu (Pompeu Fabra Barcelona) • Ciara Pike-Burke (Imperial College London) • Davide Tateo (TU Darmstadt) This one-day workshop will give interested researchers the possibility to get an overview of the current state of the field. Everyone is welcome to attend! https://www.wim.uni-mannheim.de/doering/conferences/rl-2025 No workshop fees are charged. Best wishes Leif Döring Professor Mathias Staudigl, PhD Chair for Mathematical Optimization Universität Mannheim Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik B6, C 306 | 68131 Mannheim Tel: +49 621 181 2545 E-Mail: mathias.staudigl(a)uni-mannheim.de Web: https://sites.google.com/view/mathiasstaudigl

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Special issue SPL on e-values - deadline extended to February 1st
by Heide, Rianne de (UT-EEMCS) 02 Dec '24

02 Dec '24

Call for papers: deadline extended Special issue in Statistics and Probability Letters Guest editors: Prof. Jelle Goeman and Dr. Rianne de Heide Statistics and Probability Letters is a venue for very short papers up to six pages, short notes that may be a difficult fit with other journals. When preparing a submission, you can use the provided elsarticle latex template with setting final. E-values and Multiple Testing E-values have recently emerged as a new perspective on statistical hypothesis testing, allowing anytime-valid inference. But what if we have more than one hypothesis of interest? What multiple testing methods translate well (or not so well) from p-values to e-values? How can the e-value perspective enrich multiple testing? Though some seminal contributions have already made in this area, much remains to be explored. We accept submissions about e-values with a tangential link to multiple testing. Manuscript Submission Deadline: February 1, 2025 https://www.sciencedirect.com/journal/statistics-and-probability-letters/sp…

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Machine-learning-nederland December 2024