May 2024 - SIPTA News - lists.sipta.org

[job] PhD in probabilistic ML @ TU Eindhoven
by Cassio de Campos 22 May '24

22 May '24

Dear all, We are currently looking for a PhD candidate in probabilistic machine learning; please forward the information below to any parties you think might be interested! We are particularly interested in imprecise probability related topics. best regards, cassio. --- Are you interested in working on foundational issues of robustness and reliability of probabilistic AI? Are you motivated to translate your theoretical results to real-world problems in healthcare? Then come join us as our next PhD-candidate in the Uncertainty in AI group! The Uncertainty in Artificial Intelligence (UAI) group is a growing group embedded in the Data and AI (DAI) cluster at the Eindhoven University of Technology (https://dai.win.tue.nl) In the DAI cluster, we aim at developing foundations of AI for the present and the future. This includes the design of new AI methods, development of AI algorithms, and tools with a view at expanding the reach of AI and its generalization abilities. In particular, we study foundational issues of robustness, safety, trust, reliability, tractability, scalability, interpretability, and explainability of AI. The UAI group is looking for a highly motivated and skilled PhD candidate to work in the area of probabilistic machine learning. The position is fully funded for a term of four years. The research direction will be determined together with the successful candidate and in line with the NWO Perspectief Project Personalised Care in Oncology (www.personalisedcareinoncology.nl) The research topics may include, but are not restricted to: * Probabilistic graphical models (Markov, Bayesian, credal networks) * Causality: Theory and application * Cautious AI, including imprecise probabilities * Robust stochastic processes * Tractable models and decision-making * Online/continual learning with evolving data Job requirements * Master’s degree in Mathematics, Statistics, Computer Science, or a related field * Excellent analytical and mathematical skills * Affinity with developing theoretical results * Excellent algorithmic skills (including realizing it via coding) * Excellent academic writing and presentation skills * Proficiency in English (written and spoken) * Desire to conduct excellent research and publish in high quality conferences and journals * Independent thinker and self-responsibility * Ability and desire to collaborate and work in teams * Ability and desire to support teaching and to co-supervise bachelor and master students For more information and applying (deadline 23 June 2024), please see https://jobs.tue.nl/en/vacancy/phd-in-probabilistic-machine-learning-108125… If you have further questions, please don't hesitate to contact me! Best, dr. Thomas Krak t.e.krak [at]tue.nl Assist. Prof. in UAI group Eindhoven University of Technology

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New post on the SIPTA blog
by Van Camp, Arthur 15 May '24

15 May '24

Dear IP enthusiasts, Just over one month after the previous update, the SIPTA blog<https://sipta.org/blog/> has another new blogpost. You can learn more about Loïc Adam's doctoral research<https://sipta.org/blog/thesis-loic/> on learning preferences under severe uncertainty. Kind regards, Arthur Van Camp

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PhD Position at Eindhoven University of Technology (TU/e) in the area of Uncertainty in Artificial Intelligence
by Van Camp, Arthur 08 May '24

08 May '24

PhD Position at Eindhoven University of Technology (TU/e) in the area of Uncertainty in Artificial Intelligence Deadline: 2 June 2024 The Uncertainty in Artificial Intelligence Group (https://uai.win.tue.nl) at Eindhoven University of Technology has an open position for a PhD student. The employment is full time for five years, with an intermediate evaluation (go/no-go) after nine months. The PhD research's more specific area is imprecise probability theory. The research focuses on bringing the theory of choice functions to applications. This project allows for doing fundamental research, with the aim of advancing the theory towards applications. The candidate should hold a master's degree in computer science, mathematics, or a related field. Further details about this position, and a link to the application system, can be found at https://jobs.tue.nl/en/vacancy/phd-in-uncertainty-in-artificial-intelligenc…. Applications should be submitted by 2 June 2024 or earlier. For further information, please contact Arthur Van Camp (a.van.camp(a)tue.nl<mailto:a.van.camp@tue.nl>).

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Call for Contributions - WSCL @ ECAI 2024 Workshop
by Davide Elio Ciucci 06 May '24

06 May '24

Call for Contributions Weakly Supervised and Cautious Learning (WSCL) Bridging machine learning and uncertainty management Workshop Information Co-located with the 27th European Conference on Artificial Intelligence (ECAI 2024) Location: Santiago de Compostela, Spain Dates: 19-20th October 2024 Website: https://sites.google.com/unimib.it/wscl2024/ In recent years, machine learning (ML) has been increasingly applied in a variety of settings, with promise to analyze huge and heterogeneous volumes of data, improve decisional accuracy, and ease human labor. These systems have been predominantly built based on the supervised learning paradigm, which relies on the availability of huge amounts of data, assumed to be reliably labeled by human experts. However, with this increasing interest has also come the realization that the real-world is often far from the idealized perfection assumed in the supervised paradigm: data can be missing or noisy; supervision can be costly to obtain or have veracity issues; human users could fail at appropriating technologies based on ML, due to the incapability of ML models at reliably conveying their uncertainty. As a result, recently, increasing interest has been devoted to the development of techniques capable of dealing with these issues. These include uncertainty quantification and cautious learning, where the ML models convey to the users their uncertainty to improve reliability and reduce cognitive biases; as well as weakly supervised learning and its variants, such as incomplete supervision, where only a subset of training data is labeled; imprecise supervision, where the training data is coarse-grained labeled; and inaccurate supervision, where the labels are not always true. At the same time, due to the inherently multidisciplinary nature of these issues, there has been an increasingly deepened dialogue between ML and neighboring scientific fields, such as knowledge and uncertainty representation, as well as human-computer interaction, crowd-sourcing and active learning. The aim of this workshop is to explore how machine learning and related methods can handle weak supervision and provide more cautious and reliable support in the presence of data imperfection, as well as encourage broad discussion between ML researchers and experts in neighboring related fields whose underlying principles and foundations are central to allow the functioning of systems built on ML in less than perfect real-world settings. Topics of Interest We invite all researchers in AI and neighboring disciplines interested in the interplay between Machine Learning and Uncertainty to submit their work to WSCL workshop, which will be held during 19-20 October 2024, as a satellite event to the 27th European Conference on Artificial Intelligence (ECAI 2024). We welcome submissions on topics related to both weakly supervised learning and uncertainty quantification. More generally, the topics of interest include: ● Weakly supervised learning (including, learning from noisy data, multi-instance learning, learning from imprecise data, ...); ● Uncertainty quantification (including, probabilistic and Bayesian machine learning, conformal prediction, three-way decision, set-valued approaches, possibilistic and evidential machine learning, ...); ● Uncertainty management and non-standard theories of uncertainty (including, foundations of probability theory, rough sets, fuzzy sets and possibility theory, evidence theory and belief functions, imprecise probabilities and credal sets, ...); ● Conformal prediction and related methods (including, Venn prediction, online learning, compression models, algorithmic information theory); ● Human-AI interaction and uncertainty communication; ● Active learning; ● Crowd-sourcing and data perspectivism Submission Instructions We will welcome three types of submissions: ● full original papers (up to 7 pages); ● extended abstracts (up to 4 pages); ● already published work. Each accepted submission, irrespective of the type, will be assigned an oral presentation slot. Also, articles rejected at the main track of the ECAI 2024 conference, but relevant to the topics of interest of the workshop, may be transferred at the WSCL workshop based on the ECAI reviews. Contributions should be submitted through the chairingtool platform, available at the following link: https://chairingtool.com/conferences/WSCL24/MainTrack/ Original submissions (both full papers and extended abstracts) should be formatted according to the ECAI formatting instructions, using the ECAI 2024 template and formatting requirements specified by ECAI. The purpose of workshop presentations is to get early feedback on new ideas and to stimulate discussions between researchers interested in weakly supervised and cautious learning. As such, there will be no formal proceedings, but we may post accepted papers on the workshop website for the benefit of the workshop participants if the authors agree to this (and the paper has not already been published). Also, depending on the number of accepted contributions we may decide on organizing a special issue on a relevant scientific journal. Key Dates ● Submission deadline: 30th June 2024 ● Accept/Reject communications: 18th July 2024 ● Early registration deadline: 15th August 2024 ● Workshop date: 19-20th October 2024 Organizers ● Andrea Campagner, IRCCS Ospedale Galeazzi Sant’Ambrogio (Milan, Italy), andrea.campagner(a)unimib.it <mailto:andrea.campagner@unimib.it> (primary contact person) ● Davide Ciucci, University of Milano-Bicocca (Milan, Italy), davide.ciucci(a)unimib.it <mailto:davide.ciucci@unimib.it> ● Sébastien Destercke, Heudyasic Laboratory, CNRS (Compiègne, France), sebastien.destercke(a)hds.utc.fr <mailto:sebastien.destercke@hds.utc.fr> ●Luciano Sànchez, University of Oviedo(Oviedo,Spain), luciano(a)uniovi.es <mailto:luciano@uniovi.es>

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