#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.

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

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