94 citations · 226 across the 12 of their papers we have counts for
12 papers
Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation
Hao Cheng, Shufeng Kong, Yanchen Deng +4
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-reve…
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
Yanchen Deng, Shufeng Kong, Caihua Liu +1
Belief Propagation (BP) is an important message-passing algorithm for various reasoning tasks over graphical models, including solving the Constraint Optimization Problems (COPs).…
Pretrained Cost Model for Distributed Constraint Optimization Problems
Yanchen Deng, Shufeng Kong, Bo An
Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple…
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai, Shufeng Kong, Carla P. Gomes
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 b…
Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings
Shufeng Kong, Francesco Ricci, Dan Guevarra +3
Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important…
Materials Representation and Transfer Learning for Multi-Property Prediction
Shufeng Kong, Dan Guevarra, Carla P. Gomes +1
The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine…
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Wenting Zhao, Shufeng Kong, Junwen Bai +2
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global chang…
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
Shufeng Kong, Junwen Bai, Jae Hee Lee +6
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 predicti…
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Junwen Bai, Shufeng Kong, Carla Gomes
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We…
Multiagent Simple Temporal Problem: The Arc-Consistency Approach
Shufeng Kong, Jae Hee Lee, Sanjiang Li
The Simple Temporal Problem (STP) is a fundamental temporal reasoning problem and has recently been extended to the Multiagent Simple Temporal Problem (MaSTP). In this paper we pre…
Exploring Directional Path-Consistency for Solving Constraint Networks
Shufeng Kong, Sanjiang Li, Michael Sioutis
Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally…