I'm really looking forward to finally starting this chapter and I'm excited about what's to come 😀

Classical mixture models are limited to positive weights and this requires learning very large mixtures! Can we learn (deep) mixtures with negative weights? Answer in our #ICLR2024 spotlight by Lorenzo Loconte Aleks, Martin, Stefan, Nicolas Arno Solin 📜openreview.net/forum?id=xIHi5…

The softmax bottleneck is an interesting problem; it has many side effects which we do not yet fully understand! If you want to build an intuition for the problem, here is an interactive visualisation I made grv.unargmaxable.ai/static/files/s… (best viewed on desktop).

cut holes in your densities to be more expressive! 📜 openreview.net/pdf?id=xIHi5nx… 💻 github.com/april-tools/sq…

Will be at AISTATS this week, would love to chat if you have integrals to approximate or are generally into compstat / opt. transport / estimation in causal inference. Also we have two papers around importance sampling and variational inference. 1st: adaptive IS for heavy tails

In our ICML 2024 (ICML Conference) paper, we study neurosymbolic methods under the very common independence assumption… and find many problems! 1️⃣ Non-convexity 2️⃣ Disconnected minima 3️⃣ Unable to represent uncertainty Making optimisation very challenging 😱 Let’s explore 👇

We learn more expressive mixture models that can subtract probability density by squaring them 🚨We show squaring can reduce expressiveness To tackle this we build sum of squares circuits🆘 🚀We explain why complex parameters help, and show an expressiveness hierarchy around🆘

Exciting news in the global statistics community! Grace Wahba was awarded the prestigious 2025 International Prize in Statistics for her groundbreaking work on smoothing splines, which revolutionized data analysis and machine learning. https://www. statprize.org/index.cfm