5 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
Yuche Gao, Arik Reuter, Siyuan Guo +3
Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. H…
Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI
Adrian Hayler, Klemens Flöge, Alan Arazi +44
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…
Physics of Learning: A Lagrangian perspective to different learning paradigms
Siyuan Guo, Bernhard Schölkopf
We study the problem of building an efficient learning system. Efficient learning processes information in the least time, i.e., building a system that reaches a desired error thre…
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Patrik Reizinger, Bálint Mucsányi, Siyuan Guo +3
Self-supervised feature learning and pretraining methods in reinforcement learning (RL) often rely on information-theoretic principles, termed mutual information skill learning (MI…
Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo +3
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth c…