4 papers
Parallel Complex Diffusion for Scalable Time Series Generation
Rongyao Cai, Yuxi Wan, Kexin Zhang +4
Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time seri…
Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression
Holden Lee, Kexin Zhang
We propose using a modified conductance-based method to study the mixing time of an important class of two-block Gibbs samplers, the data augmentation (DA) algorithm. %, which is o…
From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation
Rongyao Cai, Ming Jin, Qingsong Wen +1
Domain shift poses a fundamental challenge in time series analysis, where models trained on source domain often fail dramatically when applied in target domain with different yet s…
Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience
Andrew Chang, Chenkai Hu, Ji Qi +5
Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment…