9 citations · 21 across the 8 of their papers we have counts for
9 papers
MEGnifying Emotion: Sentiment Analysis from Annotated Brain Data
Brian Liu, Oiwi Parker Jones
Decoding emotion from brain activity could unlock a deeper understanding of the human experience. While a number of existing datasets align brain data with speech and with speech t…
MOSS: Multi-Objective Optimization for Stable Rule Sets
Brian Liu, Rahul Mazumder
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity,…
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficu…
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu, Rahul Mazumder
We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging. Motivated by an interesting paper…
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
Brian Liu, Rahul Mazumder
We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent add…
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
Brian Liu, Rahul Mazumder
We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. FIRE selects sp…