Machine-learning-nederland June 2022

machine-learning-nederland@list.uva.nl

4 participants
5 discussions

[jobs] Postdoc Position in Causal Inference (University of Amsterdam)
by Joris Mooij 16 Jun '22

16 Jun '22

Vacancy: Postdoctoral Researcher in Causal Inference (University of Amsterdam) ============================================================================== Are you a machine learning researcher or statistician who is passionate about causality and its applications for domain adaptation and optimization? We have a vacancy for a postdoctoral researcher 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: August 31, 2022 Preferred starting date: Autumn 2023 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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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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Machine-learning-nederland June 2022