1 citations · 1 across the 8 of their papers we have counts for
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Arrow: A Foundation Model for Causal Discovery
Ryan Thompson, He Zhao, Daniel M. Steinberg +1
We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a top…
Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems
Xuesong Wang, Michael Groom, Rafael Oliveira +3
Recent years have seen a surge in data-driven surrogates for dynamical systems that can be orders of magnitude faster than numerical solvers. However, many machine learning-based m…
Causal Preference Elicitation
Edwin V. Bonilla, He Zhao, Daniel M. Steinberg
We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over di…
Rényi Neural Processes
Xuesong Wang, He Zhao, Edwin V. Bonilla
Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their advanta…
Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs
He Zhao, Vassili Kitsios, Terence J. O'Kane +1
We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested…
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Edwin V. Bonilla, Pantelis Elinas, He Zhao +3
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian appro…