1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…