activity
20172026
most citedTransfer Learning in Information Criteria-based Feature Selection

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation

Zhe Feng, Shilong Tao, Haonan Sun +3

Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability t…

cs.LG2026

Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction

Shilong Tao, Zhe Feng, Shaohan Chen +3

Fluid-solid interaction (FSI) problems are fundamental in many scientific and engineering applications, yet effectively capturing the highly nonlinear two-way interactions remains…

cs.LG2023

Domain Knowledge integrated for Blast Furnace Classifier Design

Shaohan Chen, Di Fan, Chuanhou Gao

Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace syst…

stat.ML2021★ 2 cited

Transfer Learning in Information Criteria-based Feature Selection

Shaohan Chen, Nikolaos V. Sahinidis, Chuanhou Gao

This paper investigates the effectiveness of transfer learning based on Mallows' Cp. We propose a procedure that combines transfer learning with Mallows' Cp (TLCp) and prove that i…

stat.ML2018

Knowledge Integrated Classifier Design Based on Utility Optimization

Shaohan Chen, Chuanhou Gao

This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as t…

stat.ML2018

Efficacy of regularized multi-task learning based on SVM models

Shaohan Chen, Zhou Fang, Sijie Lu +1

This paper investigates the efficacy of a regularized multi-task learning (MTL) framework based on SVM (M-SVM) to answer whether MTL always provides reliable results and how MTL ou…

stat.ML2017★ 2 cited

Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors

Shaohan Chen, Chuanhou Gao, Ping Zhang

The lack of interpretability often makes black-box models difficult to be applied to many practical domains. For this reason, the current work, from the black-box model input port,…