4 accepted papers
This work proposes Segmenting changepoint Gaussian process regression (SegCPGP), an offline changepoint detection method that integrates Gaussian process regression with the changepoint kernel, the likelihood ratio test and binary search. We use the spectral mixture kernel to detect various types of
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a model's prediction and an average baseline to the different features used as input to the model. Being based on solid gam…
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these many-dimensional measurements as instrumental variables (instruments)…
Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given…