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Found 4 papers, 2 papers with code
Invariance & Causal Representation Learning: Prospects and Limitations
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms.
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained.
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations.
On Disentanglement in Gaussian Process Variational Autoencoders
While many models have been introduced to learn such disentangled representations, only few attempt to explicitly exploit the structure of sequential data.
