#kernelmethods

🌍 Exciting News! 🌟 🎓 Emiliano Diaz just defended his thesis "Towards Causal Discovery for Earth System Sciences" @isp_uv_es @UV_EG 🌍🛰️☁️ #CausalDiscovery #EarthScience 🤖 The advisors, colleagues, family & friends are super proud! 🥳 Thanks @ERC_Research! 🧵

This work addresses challenges in Earth system sciences data—non-additive, causal-insufficient, and spatio-temporal. Plus, it tackles instantaneous causal relationships in dynamic deterministic systems. 🌐 #CausalInference #DataScience

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

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.

Random Features revisited: DL meets Kernel methods. #randomfeatures #deeplearning #kernelmethods

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! #NeurIPS2022 #ExplainableAI #KernelMethods

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: