1 citations · 1 across the 6 of their papers we have counts for
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Affine-invariant Cubic Newton with Weak Learners
Nikita Zozoulenko, Daniel Falkowski, Thomas Cass +1
Modern gradient boosting algorithms are based on Newton's method, which excels empirically but lacks global convergence guarantees. While recent work on Gradient-Regularized Newton…
Gradient Regularized Newton Boosting Trees with Global Convergence
Nikita Zozoulenko, Daniel Falkowski, Thomas Cass +1
Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being based on Newton boosting: a secon…
Random Feature Representation Boosting
Nikita Zozoulenko, Thomas Cass, Lukas Gonon
We introduce Random Feature Representation Boosting (RFRBoost), a novel method for constructing deep residual random feature neural networks (RFNNs) using boosting theory. RFRBoost…
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
Nikita Zozoulenko, Thomas Cass, Lukas Gonon
The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in . In this work, we extend the concept of Mahalanobis distanc…