Zobrazeno 1 - 10
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pro vyhledávání: '"Liu, Brian"'
We study the following generalization of the Hamiltonian cycle problem: Given integers $a,b$ and graph $G$, does there exist a closed walk in $G$ that visits every vertex at least $a$ times and at most $b$ times? Equivalently, does there exist a conn
Externí odkaz:
http://arxiv.org/abs/2405.16270
Autor:
Panagiotaki, Efimia, Reinmund, Tyler, Mouton, Stephan, Pitt, Luke, Shanthini, Arundathi Shaji, Tubby, Wayne, Towlson, Matthew, Sze, Samuel, Liu, Brian, Prahacs, Chris, De Martini, Daniele, Kunze, Lars
This paper introduces RobotCycle, a novel ongoing project that leverages Autonomous Vehicle (AV) research to investigate how road infrastructure influences cyclist behaviour and safety during real-world journeys. The project's requirements were defin
Externí odkaz:
http://arxiv.org/abs/2403.07789
Autor:
Liu, Brian, Mazumder, Rahul
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 by \cite{mentch2020randomization}, where the authors argue that random
Externí odkaz:
http://arxiv.org/abs/2402.12668
Autor:
Liu, Brian, Mazumder, Rahul
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 additive models. The framework leverages a novel optimization procedure t
Externí odkaz:
http://arxiv.org/abs/2402.12630
During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at the University, we developed a mathematical model for estimating the risk of SARS-CoV-2 transmis
Externí odkaz:
http://arxiv.org/abs/2310.04563
Autor:
Liu, Brian, Mazumder, Rahul
Publikováno v:
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)
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 sparse representative subsets of rules from tree ensembles, that are eas
Externí odkaz:
http://arxiv.org/abs/2306.07432
Existing data collection methods for traffic operations and control usually rely on infrastructure-based loop detectors or probe vehicle trajectories. Connected and automated vehicles (CAVs) not only can report data about themselves but also can prov
Externí odkaz:
http://arxiv.org/abs/2208.02792
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this package first build large tree ensembles that prioritize basis functions wi
Externí odkaz:
http://arxiv.org/abs/2207.03935
Autor:
Liu, Brian, Mazumder, Rahul
Publikováno v:
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023
Tree ensembles are powerful models that achieve excellent predictive performances, but can grow to unwieldy sizes. These ensembles are often post-processed (pruned) to reduce memory footprint and improve interpretability. We present ForestPrune, a no
Externí odkaz:
http://arxiv.org/abs/2206.00128
Autor:
Russell, Matthew W. *, Maatouk, Christopher M., Kim, Suzie *, Liu, Brian, Muste, Justin C., Talcott, Katherine E., Singh, Rishi P. *
Publikováno v:
In Canadian Journal of Ophthalmology/Journal canadien d'ophtalmologie October 2024 59(5):e590-e595