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📢Call for Reading 📜 Kernel #VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space 🔗 brnw.ch/21x0v4E #UniversityofWaterloo #SelfSupervisedLearning #VICReg #KernelMethods #RKHS #RepresentationLearning #MachineLearning #DeepLearning #AI
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Work #1 explores #kernel methods for bivariate causal discovery using Conditional Mean Embedding (CME). A parsimony-based method helps infer causal direction by comparing complexity measures of CME sets. 📊 #KernelMethods #CausalDiscovery Great collab with @sejDino
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29 Nov 2022
Interested in explaining kernel methods (or GP) with Shapley values? Or in using SVs as attribution priors in ERM? I’ve got you covered! Check out my poster on RKHS-SHAP at Hall J #433 tmrw from 4-6pm CST! nips.cc/virtual/2022/poster/… #NeurIPS2022 #ExplainableAI #KernelMethods

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New #SpecialIssue "Entropy: The Cornerstone of Machine Learning" edited by Prof. Dr. Viacheslav Kovtun, Prof. Dr. Krzysztof Grochla and Prof. Dr. Jerry D. Gibson is open for submission! mdpi.com/journal/entropy/spe… #MachineLearning #EnsembleModels #KernelMethods
The second hands-on tutorial has started at #ML4M. Milica Todorovic presenting kernel methods for potential fitting. 🎯 #gaussianprocess #machinelearning #kernelmethods
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12 Nov 2021
Yesterday was a day very much circling around notions of quantum-assisted machine learning. After #QTML2021, Sofiene Jerbi from Innsbruck gave a wonderful talk on #quantummachinelearning beyond #kernelmethods in our reading group.
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Christopher Ré, @StanfordAILab on 'Theory and systems for weak supervision" - join him and Live Q&A next Tuesday #DeepLearning #kernelmethods @naverlabseurope Open seminar series 📆 Nov 17th 4pm CET Registration: bit.ly/3ksOZV5
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#mdpientropy #Specialissue calling for papers: "#Information Theoretic Learning and Kernel Methods", edited by Prof. Badong Chen and Prof. Jose C. Principe 👉👉👉mdpi.com/journal/entropy/spe… #machinelearning #kernelmethods #entropy #nonlinearsystems Welcome your submissions!😀😀😀
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