Check this out 📢 Score-based diffusion models are powerful—but slow to sample. Could there be something better? Drop the scores, use proximals instead! We present Proximal Diffusion Models, providing a faster alternative both in theory* and practice. Here’s how it works 🧵(1/n)

Awesome to see our cover in @cp-patterns.bsky.social finally out! And kudos go to Zhenzhen Wang for her massive work on biomarker discovery for breast cancer www.cell.com/patterns/ful...

Today, on #WomenInScience day, this paper on biomarker discovery for breast cancer, by my amazing student Zhenzhen, has just appeared in @cp-patterns.bsky.social 🎉 Her work shows how to construct fully interpretable biomarkers employing bi-level graph learning! @jhu.edu @hopkinsdsai.bsky.social

Nice write-up by @JHUCompSci about @JacopoTeneggi's work. Puch-line: interpretability of opaque ML models can be posed as hypothesis tests, for which online (efficient) testing procedures can be derived! www.cs.jhu.edu/news/wanna-b...

📣 What should *ML explanations* convey, and how does one report these precisely and rigorously? @neuripsconf.bsky.social come check Jacopo Teneggi's work on Testing for Explanations via betting this afternoon! I *bet* you'll like it :) openreview.net/pdf?id=A0HSm... @hopkinsdsai.bsky.social

NeurIPS paper: Excited for our work (with Iuliia Dmitrieva+Sergey Babkin) on "realSEUDO for real-time calcium imaging analysis" arxiv.org/abs/2405.15701 to be presented tomorrow (Thu 4:30-7:30PM). realSEUDO is a fully on-line method for cell detection and activity estimation that runs at >30Hz.