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Found 4 papers, 3 papers with code
Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
This research heralds a paradigm shift in machine learning, paving the way for a new era of robust and precise classification across diverse real-world applications.
NCART: Neural Classification and Regression Tree for Tabular Data
Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning.
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP).
TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method
Gradient Boosting Machines (GBMs) have demonstrated remarkable success in solving diverse problems by utilizing Taylor expansions in functional space.
