Check our new @aistats_conf paper:
Fast & Robust Simulation-Based Inference with Optimization Monte Carlo
TL;DR: importance sampling with proposals centered on high-posterior regions via gradient descent.
The simplicity of ABC, without the curse of dimensionality. 🧵
Explainable AI | Uncertainty Quantification | PhD Student @ HUA | Research Assistant @ ATHENA RC
- 📌 [Effector](github.com/givasile/effec…) keeps evolving and is now integrated into the AIDAPT-EU project as the main explainability package. 📌 Learn more about it, in this Medium post: [Effector on Medium](medium.com/@ntipakos/effe…)EFFECTOR—AI-DAPT’s framework for interpretable AI ✅ Global & regional effects to understand model decisions ✅ Seamless integration with popular ML libraries ✅ Faster, deeper insights for tabular data ai-dapt.eu/effector/ ⭐ the project on GitHub: github.com/givasile/effec…
- 🌟 Excited to announce the release of Effector 🌟 🚀a Python package for global and regional explanations on tabular data. 🚀 if you find it useful, please star the repo github.com/givasile/effec… @cdiou , @Theodore_D, @entoutsi, @GiuCasalicchio , @JuliaHerbinger , @BBischl
- Very good opportunity for anyone interested in XAI!Passionate about diving into the world of Interpretable Machine Learning and Explainable AI for a PhD at @LMU_Muenchen? Apply now! 🎓 @XAI_Research #PhD #XAI #MachineLearning #Statistics #ExplainableAI #DataScience #InterpretableML job-portal.lmu.de/jobposting/09f…



