3 accepted papers
Estimating counterfactual outcomes from observational data is critical for informed decision-making in domains such as personalized marketing, healthcare, and online platforms. In these contexts, decision processes frequently involve high-dimensional combinatorial interventions, including bundled ch
Inference from complex distributions is a common problem in machine learning needed for many Bayesian methods. We propose an efficient, gradient-free method for learning general GMM approximations of multimodal distributions based on recent insights from stochastic search methods. Our method establi…
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regular…