7 citations · 9 across the 13 of their papers we have counts for
14 papers
Beyond Additive Decompositions: Interpretability Through Separability
Jinyang Liu, Munir Eberhardt Hiabu
Interpretable machine learning requires models that are accurate and structurally faithful to the data. Existing explainability methods rely heavily on additive representations (e.…
Fast Estimation of Partial Dependence Functions using Trees
Jinyang Liu, Tessa Steensgaard, Marvin N. Wright +2
Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the…
Pure interaction effects unseen by Random Forests
Ricardo Blum, Munir Hiabu, Enno Mammen +1
Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the…
A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving
Munir Hiabu, Emil Hofman, Gabriele Pittarello
We introduce new approaches for forecasting IBNR (Incurred But Not Reported) frequencies by leveraging individual claims data, which includes accident date, reporting delay, and po…
Identifiability and estimation of the competing risks model under exclusion restrictions
Munir Hiabu, Simon M. S. LU, Ralf A. Wilke
The non-identifiability of the competing risks model requires researchers to work with restrictions on the model to obtain informative results. We present a new identifiability sol…
Consistency of Random Forest Type Algorithms under a Probabilistic Impurity Decrease Condition
Ricardo Blum, Munir Hiabu, Enno Mammen +1
This paper derives a unifying theorem establishing consistency results for a broad class of tree-based algorithms. It improves current results in two aspects. First of all, it can…
Smooth Backfitting for Additive Hazard Rates
Stephan M. Bischofberger, Munir Hiabu, Enno Mammen +1
Smooth backfitting was first introduced in an additive regression setting via a direct projection alternative to the classic backfitting method by Buja, Hastie and Tibshirani. This…
Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle
Gabriele Pittarello, Munir Hiabu, Andrés M. Villegas
This paper introduces yet another stochastic model replicating chain-ladder estimates and furthermore considers extensions that add flexibility to the modeling. In its simplest for…
Unifying local and global model explanations by functional decomposition of low dimensional structures
Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a ne…
Local linear smoothing in additive models as data projection
Munir Hiabu, Enno Mammen, Joseph T. Meyer
We discuss local linear smooth backfitting for additive non-parametric models. This procedure is well known for achieving optimal convergence rates under appropriate smoothness con…
Random Planted Forest: a directly interpretable tree ensemble
Munir Hiabu, Enno Mammen, Joseph T. Meyer
We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional deco…
Non-Smooth Backfitting for Excess Risk Additive Regression Model with Two Survival Time-Scales
Munir Hiabu, Jens P. Nielsen, Thomas H. Scheike
We present a new backfitting algorithm estimating the complex structured non-parametric survival model of Scheike (2001) without having to use smoothing. The considered model is a…
Continuous chain-ladder with paid data
Stephan M. Bischofberger, Munir Hiabu, Alex Isakson
We introduce a continuous-time framework for the prediction of outstanding liabilities, in which chain-ladder development factors arise as a histogram estimator of a cost-weighted…
Smooth backfitting of proportional hazards with multiplicative components
Munir Hiabu, Enno Mammen, Maria Dolores Martinez-Miranda +1
Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space…