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…
Adaptive Data Selection for Multi-Layer Perceptron Training: A Sub-linear Value-Driven Method
Xiyang Zhang, Chen Liang, Haoxuan Qiu +1
Data selection is one of the fundamental problems in neural network training, particularly for multi-layer perceptrons (MLPs) where identifying the most valuable training samples f…
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…
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
Zhiyu Liang, Dongrui Cai, Chenyuan Zhang +6
Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous…
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…
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Zhiyu Liang, Jianfeng Zhang, Chen Liang +3
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable represe…