6 citations · 7 across the 6 of their papers we have counts for
8 papers
Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Dingling Yao, Kahaan Gandhi, Valentin Duruisseaux +3
Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on ob…
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…
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu, Dingling Yao, Sébastien Lachapelle +4
Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-d…
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao, Danru Xu, Sébastien Lachapelle +5
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partial…