Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Kong, Shufeng"'
Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to ma
Externí odkaz:
http://arxiv.org/abs/2312.06443
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property predictions, while more challenging spectral properties remain less e
Externí odkaz:
http://arxiv.org/abs/2302.01486
Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs). It has been shown that BP can achieve state-of-the-art performance on
Externí odkaz:
http://arxiv.org/abs/2209.12000
Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple autonomous agents. Previously, Machine Learning (ML) has been largely
Externí odkaz:
http://arxiv.org/abs/2112.04187
Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model based on a Gaussian mixture variational autoencoder (C-GMVAE), which le
Externí odkaz:
http://arxiv.org/abs/2112.00976
Autor:
Kong, Shufeng, Ricci, Francesco, Guevarra, Dan, Neaton, Jeffrey B., Gomes, Carla P., Gregoire, John M.
Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of
Externí odkaz:
http://arxiv.org/abs/2110.11444
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the under
Externí odkaz:
http://arxiv.org/abs/2106.02225
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global change. A central goal for conservation planning and monitoring is the abil
Externí odkaz:
http://arxiv.org/abs/2103.06375
Autor:
Kong, Shufeng, Bai, Junwen, Lee, Jae Hee, Chen, Di, Allyn, Andrew, Stuart, Michelle, Pinsky, Malin, Mills, Katherine, Gomes, Carla P.
A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously mo
Externí odkaz:
http://arxiv.org/abs/2010.16040
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-label classification, Multivariate
Externí odkaz:
http://arxiv.org/abs/2007.06126