182 citations · 329 across the 14 of their papers we have counts for
15 papers
Robust Loss Functions for Training Decision Trees with Noisy Labels
Jonathan Wilton, Nan Ye
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 o…
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1
The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing goo…
Adaptive Discretization using Voronoi Trees for Continuous POMDPs
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1
Solving continuous Partially Observable Markov Decision Processes (POMDPs) is challenging, particularly for high-dimensional continuous action spaces. To alleviate this difficulty,…
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko +2
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 wel…
Adaptive Discretization using Voronoi Trees for Continuous-Action POMDPs
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese +1
Solving Partially Observable Markov Decision Processes (POMDPs) with continuous actions is challenging, particularly for high-dimensional action spaces. To alleviate this difficult…
A Boosting Algorithm for Positive-Unlabeled Learning
Yawen Zhao, Mingzhe Zhang, Chenhao Zhang +3
Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural n…