research
∙
07/20/2023
Spectral co-Clustering in Rank-deficient Multi-layer Stochastic co-Block Models
Modern network analysis often involves multi-layer network data in which...
share
research
∙
07/10/2023
FedYolo: Augmenting Federated Learning with Pretrained Transformers
The growth and diversity of machine learning applications motivate a ret...
research
∙
06/18/2023
2D-Shapley: A Framework for Fragmented Data Valuation
Data valuation – quantifying the contribution of individual data sources...
research
∙
04/14/2023
Subsampling-Based Modified Bayesian Information Criterion for Large-Scale Stochastic Block Models
Identifying the number of communities is a fundamental problem in commun...
research
∙
01/06/2023
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks
Identifying functional connectivity biomarkers of major depressive disor...
research
∙
10/30/2022
Robust Data Valuation via Variance Reduced Data Shapley
Data valuation, especially quantifying data value in algorithmic predict...
research
∙
06/30/2022
On the efficacy of higher-order spectral clustering under weighted stochastic block models
Higher-order structures of networks, namely, small subgraphs of networks...
research
∙
05/11/2022
Learning Multitask Gaussian Bayesian Networks
Major depressive disorder (MDD) requires study of brain functional conne...
research
∙
09/21/2021
Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making
Scoring systems, as simple classification models, have significant advan...
research
∙
12/16/2020
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Deep networks are typically trained with many more parameters than the s...
research
∙
09/03/2020
Kernel Interpolation of High Dimensional Scattered Data
Data sites selected from modeling high-dimensional problems often appear...
research
∙
04/25/2020
Randomized spectral co-clustering for large-scale directed networks
Directed networks are generally used to represent asymmetric relationshi...
research
∙
03/08/2020
Angle-Based Cost-Sensitive Multicategory Classification
Many real-world classification problems come with costs which can vary f...
research
∙
01/20/2020
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Spectral clustering has been one of the widely used methods for communit...
research
∙
01/09/2020
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
In this paper, we propose an adaptive stopping rule for kernel-based gra...
research
∙
11/29/2017
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion
We present our preliminary work to determine if patient's vocal acoustic...
research
∙
02/28/2017
Learning rates for classification with Gaussian kernels
This paper aims at refined error analysis for binary classification usin...
research
∙
03/31/2014
