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…
Generating Financial Time Series by Matching Random Convolutional Features
Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2
Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especi…
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…