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Found 6 papers, 0 papers with code
A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles
This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.
Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS).
Infinitesimal gradient boosting
We define infinitesimal gradient boosting as a limit of the popular tree-based gradient boosting algorithm from machine learning.
Hidden regular variation for point processes and the single/multiple large point heuristic
We consider regular variation for marked point processes with independent heavy-tailed marks and prove a single large point heuristic: the limit measure is concentrated on the cone of point measures with one single point.
Point Processes
Probability
Behavior of linear L2-boosting algorithms in the vanishing learning rate asymptotic
We investigate the asymptotic behaviour of gradient boosting algorithms when the learning rate converges to zero and the number of iterations is rescaled accordingly.
A Semi-Definite Programming approach to low dimensional embedding for unsupervised clustering
This paper proposes a variant of the method of Gu\'edon and Verhynin for estimating the cluster matrix in the Mixture of Gaussians framework via Semi-Definite Programming.
