- Machine-learning-nederland

YES workshop on "Optimal Transport, Statistics, Machine Learning and moving in between"
by Pires da Silva Castro, Rui 13 Jun '22

13 Jun '22

Dear Colleagues, The YES workshop "Optimal Transport, Statistics, Machine Learning and moving in between” will take place 5-9 September 2022 in Eurandom, Eindhoven. This workshop is part of the series of Young European Statistician workshops, but has a broader scope than usual. We are very happy to feature tutorial talks by world experts Marco Cuturi (CREST-ENSAE, Apple ML Research), Jonathan Niles-Weed (NYU) and Yoav Zemel (University of Cambridge). In addition to this, there will be talks by several invited speakers. Young researchers will be given the opportunity to present their work in the format of contributed talks or posters (see call for abstracts on the page below). For more information and registration, please visit the conference website: https://www.eurandom.tue.nl/event/workshop-yes-optimal-transport-statistics…<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.euran…> Best wishes, The organizers (Rui Castro, Augusto Gerolin, Johannes Schmidt-Hieber, Oliver Tse)

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Reminder: Thematic seminar: Julia Olkhovskaya, Today, 16h00
by Tim van Erven 10 Jun '22

10 Jun '22

Dear all, Gentle reminder: the talk by Julia Olkhovskaya in the thematic seminar is today! *Julia Olkhovskaya *(Vrije Universiteit, https://sites.google.com/view/julia-olkhovskaya/home) *Friday June 10*, 16h00-17h00 Online on Zoom: https://uva-live.zoom.us/j/89796690874 Meeting ID: 897 9669 0874 *Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits* We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspective of Russo and Van Roy [2016] to the contextual setting by introducing a new concept of information ratio based on the mutual information between the unknown model parameter and the observed loss. This allows us to bound the regret in terms of the entropy of the prior distribution through a remarkably simple proof, and with no structural assumptions on the likelihood or the prior. We also extend our results to priors with infinite entropy under a Lipschitz assumption on the log-likelihood. An interesting special case is that of logistic bandits with d-dimensional parameters, K actions, and Lipschitz logits. This is joint work with Gergely Neu, Matteo Papini and Ludovic Schwartz. 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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UAI 2022 in Eindhoven
by Marloes Maathuis 08 Jun '22

08 Jun '22

Dear all, We are excited to announce that the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022; https://www.auai.org/uai2022) will be held in a hybrid format in Eindhoven, The Netherlands, on August 1-5, 2022. We will have tutorials on August 1, the main conference on August 2-4, and workshops on August 5. The main conference is single-track and accepted 36 papers for oral presentation and 194 for poster presentation. Below we would like to share some updates: 1. Registration is open at https://www.auai.org/uai2022/registration. The early bird deadline is June 21st. 2. Students are encouraged to apply for student scholarships. The deadline for this is June 15. For more information, please see https://www.auai.org/uai2022/student_scholarships. 3. We are delighted to have Danilo J. Rezende, Eric P. Xing, Finale Doshi-Velez, Mihaela van der Schaar, Peter Spirtes, and Zeynep Akata as keynote speakers. 4. The UAI 2022 competition ( https://www.auai.org/uai2022/uai2022_competition) will be starting soon. We hope to see many of you at UAI this year, either in-person or online! Best regards, James Cussens & Kun Zhang UAI 2022 Program Chairs and Cassio de Campos & Marloes Maathuis UAI 2022 General Chairs

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Reminder: Thematic seminar: Julia Olkhovskaya, Friday June 10, 16h00
by Tim van Erven 07 Jun '22

07 Jun '22

Dear all, This Friday June 10 we have Julia Olkhovskaya from the VU speaking in the thematic seminar. *Julia Olkhovskaya *(Vrije Universiteit, https://sites.google.com/view/julia-olkhovskaya/home) *Friday June 10*, 16h00-17h00 Online on Zoom: https://uva-live.zoom.us/j/89796690874 Meeting ID: 897 9669 0874 *Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits* We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspective of Russo and Van Roy [2016] to the contextual setting by introducing a new concept of information ratio based on the mutual information between the unknown model parameter and the observed loss. This allows us to bound the regret in terms of the entropy of the prior distribution through a remarkably simple proof, and with no structural assumptions on the likelihood or the prior. We also extend our results to priors with infinite entropy under a Lipschitz assumption on the log-likelihood. An interesting special case is that of logistic bandits with d-dimensional parameters, K actions, and Lipschitz logits. This is joint work with Gergely Neu, Matteo Papini and Ludovic Schwartz. 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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Thematic seminar: Julia Olkhovskaya, Friday June 10, 16h00
by Tim van Erven 30 May '22

30 May '22

Dear all, On Friday June 10 we have Julia Olkhovskaya from the VU speaking in the thematic seminar. *Julia Olkhovskaya *(Vrije Universiteit, https://sites.google.com/view/julia-olkhovskaya/home) *Friday June 10*, 16h00-17h00 Online on Zoom: https://uva-live.zoom.us/j/89796690874 Meeting ID: 897 9669 0874 *Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits* We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspective of Russo and Van Roy [2016] to the contextual setting by introducing a new concept of information ratio based on the mutual information between the unknown model parameter and the observed loss. This allows us to bound the regret in terms of the entropy of the prior distribution through a remarkably simple proof, and with no structural assumptions on the likelihood or the prior. We also extend our results to priors with infinite entropy under a Lipschitz assumption on the log-likelihood. An interesting special case is that of logistic bandits with d-dimensional parameters, K actions, and Lipschitz logits. This is joint work with Gergely Neu, Matteo Papini and Ludovic Schwartz. 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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Best Paper Award (CHF 500) deadline June 2022 - [Robotics] (ISSN 2218-6581, citescore 3.5)
by Charlene Dong 06 May '22

06 May '22

Dear colleagues, *Best Paper Award* in Special Issue "Women in Robotics" (500CHF) https://www.mdpi.com/journal/robotics/awards/1658 Deadline: 31 August 2022 You are welcome to submit articles by at least one female co-author to this special issue "Women in Robotics" https://www.mdpi.com/journal/robotics/special_issues/Women_Robotics Guest editors: Prof. Dr. Naira Hovakimyan, Prof. Dr. Anna Esposito and Prof. Dr. Sanaz Mostaghim Deadline for manuscript submissions: 15 June 2022 Each award nominee will be assessed on her paper's originality, quality, and contribution to the field by the Evaluation Committee. The winner will receive a certificate, an award of 500 CHF, and an opportunity to publish her next submission in Robotics free of charge. If you are interested in this project, please feel free to contact us via email charlene.dong(a)mdpi.com or robotics(a)mdpi.com within two weeks. Thanks for your support. Look forward to hearing from you. Best regards, Ms. Charlene Dong Managing Editor Email: charlene.dong(a)mdpi.com Robotics (http://www.mdpi.com/journal/robotics/)

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General Mathematics Colloquium (KdVI) - Max Welling - April 29
by Eni Musta 25 Apr '22

25 Apr '22

Dear colleagues, You are cordially invited to attend the next lecture in the KdVI General Mathematics Colloquium on Friday, April 29 at 16.00. Max Welling (UvA) will speak about "How GNNs and Symmetries can help to solve PDEs" (see abstract below). The lecture will be given in room C1.110 (Science Park 904) and can also be joined via Zoom https://uva-live.zoom.us/j/84560653677<https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fuva-live.…> If you are interested in receiving the announcements for future talks at the KdVI General Mathematics Colloquium, please subscribe to our mailing list https://list.uva.nl/mailman/listinfo/kdvi-math-colloq. We hope to see you then! Best wishes, Eni (on behalf of the colloquium organizers) ----------------------------------------------------- Abstract: Deep learning has seen amazing advances over the past years, completely replacing traditional methods in fields such as speech recognition, natural language processing, image and video analysis and so on. A particularly versatile deep architecture that has gained much traction lately is the graph neural network (GNN), of which transformers represent a special case. GNNs have the desirable property that they can process graph structured data while respecting permutation symmetry. Recently, GNNs have found new applications in scientific computation, for instance to predict the properties of molecules or to predict the forces that act on atoms when they evolve (e.g. fold). In this application it is also key that geometric symmetries, such as translation and rotation symmetries are taken into consideration. Professor Max Welling will report on yet another exciting application of using GNNs to solve partial differential equations (PDEs). It turns out that GNNs are an excellent tool to develop neural PDE integrators. Moreover, PDEs are full of surprising symmetries that can be leveraged to train neural integrators with less data. Professor Max Welling will discuss this very exciting new chapter in deep learning. He will end with a discussion of whether reversely, PDEs can also serve as a model for new deep architectures. Joint work with Johannes Brandstetter and Daniel Worrall.

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Today: Thematic seminar: Tor Lattimore, Friday April 22, 16h00
by Tim van Erven 22 Apr '22

22 Apr '22

Dear all, Today, April 22, we have Tor Lattimore from DeepMind speaking in the thematic seminar. *Tor Lattimore *(DeepMind, http://tor-lattimore.com) *Friday April 22*, 16h00-17h00 Online on Zoom: *https://uva-live.zoom.us/j/88233925917* Meeting ID: 882 3392 5917 *Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret * The information ratio developed by Russo and Van Roy (2014) is a powerful tool that was recently used to derive upper bounds on the regret for challenging sequential decision-making problems. I will talk about how a generalised version of this machinery can be used to derive lower bounds and give an application showing that a version of mirror descent is minimax optimal for partial monitoring using Rustichini's definition of regret. Seminar organizers: Tim van Erven Botond Szabo https://mschauer.github.io/StructuresSeminar/ *Upcoming talks: *Jun. 10,***Julia Olkhovskaya <https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit -- Tim van Erven<tim(a)timvanerven.nl> www.timvanerven.nl

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Reminder: Thematic seminar: Tor Lattimore, Friday April 22, 16h00
by Tim van Erven 18 Apr '22

18 Apr '22

Dear all, Happy Easter! This Friday, April 22, we have Tor Lattimore from DeepMind speaking in the thematic seminar. *Tor Lattimore *(DeepMind, http://tor-lattimore.com) *Friday April 22*, 16h00-17h00 Online on Zoom: *https://uva-live.zoom.us/j/88233925917* Meeting ID: 882 3392 5917 *Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret * The information ratio developed by Russo and Van Roy (2014) is a powerful tool that was recently used to derive upper bounds on the regret for challenging sequential decision-making problems. I will talk about how a generalised version of this machinery can be used to derive lower bounds and give an application showing that a version of mirror descent is minimax optimal for partial monitoring using Rustichini's definition of regret. Seminar organizers: Tim van Erven Botond Szabo https://mschauer.github.io/StructuresSeminar/ *Upcoming talks: *Jun. 10,***Julia Olkhovskaya <https://sites.google.com/view/julia-olkhovskaya/home>*, Vrije Universiteit -- Tim van Erven<tim(a)timvanerven.nl> www.timvanerven.nl

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PostDoc on Reinforcement Learning in the Real World @ TU Delft
by Frans Oliehoek 12 Apr '22

12 Apr '22

(apologies for cross posting) At Delft University of Technology, we have a vacancy for a 3 year PostDoc on Reinforcement Learning in the Real World This is in the context of the Mercury Machine Learning Lab, jointly with the University of Amsterdam and booking.com. We will focus on fundamental techniques in reinforcement learning, moticated by real-world problems. Possible directions of interest are: Bayesian reinforcement learning Multiagent / concurrent reinforcement learning Causal reinforcement learning Full vacancy text can be found here: https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?… Please forward to potential candidates, and contact Matthijs Spaan or myself in case of questions. -- _______________________________________________ Dr. Frans Oliehoek Associate Professor Delft University of Technology E-mail: f.a.oliehoek(a)tudelft.nl www.fransoliehoek.net

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Machine-learning-nederland