If you're at ICML 2026, check out Valentyn's work on correcting posterior estimations from causal foundation models!

Valentyn Melnychuk 🇺🇦@valik-melnychuk.bsky.social · 4mo ago

[1/4] 🇰🇷 Excited to present our new paper “Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference” at ICML 2026 in Seoul. The paper asks a simple question: when PFNs are used for causal inference, do their uncertainty estimates actually agree with classical frequentist inference?

My students and I will be at #NeurIPS2025 and EurIPS from Dec 2-8. My students and l will be presenting three papers.

🚨 Introducing CausalPFN, a foundation model trained on simulated data for in-context causal effect estimation, based on prior-fitted networks (PFNs). Joint work with Hamid Kamkari, Layer6AI & @rahulgk.bsky.social 🧵[1/7] 📝 arxiv.org/abs/2506.07918 🔗 github.com/vdblm/Causal... 🗣️Oral@ICML SIM workshop

My student, Ethan Choi, will be at #ICLR2025 presenting Contextual Finetuning (CFT) and teaching LLMs how to learn (joint work with Muhammad Adil Asif, Ziwen Han, John Willes @vectorinstitute.ai) 🌟Project page: younwoochoi.github.io/cft-iclr/ #239, April 26 10-12:30(Hall3,2B)

📣T-CAIREM member @rahulgk.bsky.social's presentation is online! From Associational to Causal Predictions with #DeepLearning: An examination of recent advances in bridging the gap between associative #neuralnetworks and causal reasoning. 🎥 www.youtube.com/watch?v=yE6S...

Rahul G. Krishnan | From associational to causal predictions with deep learning

YouTube video by Schwartz Reisman Institute

youtube.com

Come by tomorrow to hear about what we have been up to!

Schwartz Reisman Institute for Technology and Society@torontosri.bsky.social · 2y ago

🚨 Tomorrow! 🚨 SRI Seminar Series welcomes @rahulgk.bsky.social assistant prof. at the @uoftcompsci.bsky.social. He’s a Canada Research Chair in Computational Medicine and an AI expert at the @vectorinstitute.ai 🗓️ January 29, 2025 | 12:30 - 2:00 PM ET 🔗 www.eventbrite.ca/e/sri-semina...

o3 is incredible! Since we've maxed out scale and $$$ on scaling inference-time compute I hope we now get back to thinking about the right combination of neural nets and algorithm to performant models cheaper, faster, and more reliably.

1/6 Presenting "Unlearning Tabular Data without a 'Forget Set'"! We explore a new unlearning algorithm RELOAD in tabular learning. Drop by @neuripsconf.bsky.social Workshop on Table Representation Learning (@trl-research.bsky.social): - SAT 14 Dec from 2:30pm-3:15pm! - East Meeting Room 11-12

Come by our poster today to learn about decision making under unobserved confounding!

@vahidbalazadeh.bsky.social · 2y ago

How can we use offline expert data with unobserved confounding to guide exploration in RL? Our approach is to learn prior distributions from expert data and follow posterior sampling Come to our poster #NeurIPS2024 today to learn more! 🗓️ Thu 12 Dec 4:30 - 7 pm PST 📍 West Ballroom A-D #6708 (1/5)

First post! I’ll be at @NeurIPSConf #NeurIPS2024 until Sunday. I'd love to chat about causality for medicine & science. I'm also looking for a postdoc interested in experimental design for medicine, if that's you, send me a message. I'll be presenting two papers at the main conference. 🧵(1/7)