5 papers
The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
Dingling Yao, Andrea Polesello, Adeel Pervez +2
While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invaria…
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
Dingling Yao, Shimeng Huang, Riccardo Cadei +2
Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and high-dimensionality of real-wo…
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
Jiale Chen, Dingling Yao, Adeel Pervez +2
We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences…
Unifying Causal Representation Learning with the Invariance Principle
Dingling Yao, Dario Rancati, Riccardo Cadei +2
Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect o…
Marrying Causal Representation Learning with Dynamical Systems for Science
Dingling Yao, Caroline Muller, Francesco Locatello
Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements. However, most progress has focused on proving identifiab…