Jonathan Wilton — accepted AI-conference papers

2024

Robust Loss Functions for Training Decision Trees with Noisy Labels

AAAI 2024technical

We consider training decision trees using noisily labeled data, focusing on loss functions that can lead to robust learning algorithms. Our contributions are threefold. First, we offer novel theoretical insights on the robustness of many existing loss functions in the context of decision tree learni…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2022

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

Jonathan Wilton, Abigail Koay, Ryan Ko, Miao Xu, Nan Ye

NeurIPS 2022accept

The need to learn from positive and unlabeled data, or PU learning, arises in many applications and has attracted increasing interest. While random forests are known to perform well on many tasks with positive and negative data, recent PU algorithms are generally based on deep neural networks, and t…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗