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cs.LG2025
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur +2
We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to…
cs.LG2024
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
Goutham Rajendran, Simon Buchholz, Bryon Aragam +2
To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causa…
cs.LG2024
Identifying General Mechanism Shifts in Linear Causal Representations
Tianyu Chen, Kevin Bello, Francesco Locatello +2
We consider the linear causal representation learning setting where we observe a linear mixing of unknown latent factors, which follow a linear structural causal model. Recent…