110 citations · 111 across the 7 of their papers we have counts for
7 papers
Nearly Optimal Bayesian Inference for Structural Missingness
Chen Liang, Donghua Yang, Yutong Zhao +9
Structural missingness breaks 'just impute and train': values can be undefined by causal or logical constraints, and the mask may depend on observed variables, unobserved variables…
Revisiting Data Analysis with Pre-trained Foundation Models
Chen Liang, Donghua Yang, Zheng Liang +6
Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and vari…
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem
Huaiyuan Liu, Xianzhang Liu, Donghua Yang +5
The Maximum Minimal Cut Problem (MMCP), a NP-hard combinatorial optimization (CO) problem, has not received much attention due to the demanding and challenging bi-connectivity cons…
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning
Chen Liang, Donghua Yang, Zhiyu Liang +4
In recent times, the field of unsupervised representation learning (URL) for time series data has garnered significant interest due to its remarkable adaptability across diverse do…
Duet: efficient and scalable hybriD neUral rElation undersTanding
Kaixin Zhang, Hongzhi Wang, Yabin Lu +4
Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches have faced the workload drift pr…
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
Huaiyuan Liu, Xianzhang Liu, Donghua Yang +4
Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing de…
LSTM-based Flow Prediction
Hongzhi Wang, Yang Song, Shihan Tang
In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed. The algorith…