Simon Bing
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View article: Sanity Checking Causal Representation Learning on a Simple Real-World System
Sanity Checking Causal Representation Learning on a Simple Real-World System Open
We evaluate methods for causal representation learning (CRL) on a simple, real-world system where these methods are expected to work. The system consists of a controlled optical experiment specifically built for this purpose, which satisfi…
View article: Invariance & Causal Representation Learning: Prospects and Limitations
Invariance & Causal Representation Learning: Prospects and Limitations Open
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms. While this principle has been utilized for inference in settings where the causal variables are observed, theoretical insights when the variable…
View article: Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions Open
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained. As such, recent works to address this problem focus on various assump…
View article: Conditional generation of medical time series for extrapolation to underrepresented populations
Conditional generation of medical time series for extrapolation to underrepresented populations Open
The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact o…
View article: Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations Open
The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact o…
View article: On Disentanglement in Gaussian Process Variational Autoencoders
On Disentanglement in Gaussian Process Variational Autoencoders Open
Complex multivariate time series arise in many fields, ranging from computer vision to robotics or medicine. Often we are interested in the independent underlying factors that give rise to the high-dimensional data we are observing. While …