From the 1 of 9 linked papers with an AI index.
9 papers
Causal Foundation Models with Continuous Treatments
Christopher Stith, Medha Barath, Vahid Balazadeh +2
The paper introduces a causal foundation model that can predict individual treatment-response curves for continuous interventions, using a transformer trained on a synthetic causal…
Causal methods for LLM development and evaluation
Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10
Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and evaluation pipelines. Here,…
From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data
Mohammad R. Rezaei, Rahul G. Krishnan
A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on structured data but operate as…
Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach
Herman Bergström, Aditya Mehrotra, Rahul G. Krishnan
We introduce CoMET, \textit{\textbf{C}omposing \textbf{M}odality \textbf{E}ncoders with \textbf{T}abular foundation models}, a simple yet highly competitive method for multimodal c…
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
Mohammad R. Rezaei, Tejas Balaji, Rahul G. Krishnan
Irregularly sampled multivariate event streams remain a stubbornly difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals v…
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Medha Barath +2
The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification impossible. Existing approac…