5 papers
Diffusion Models with Double Guidance: Generate with aggregated datasets
Yanfeng Yang, Kenji Fukumizu
Creating large-scale datasets for training high-performance generative models is often prohibitively expensive, especially when associated attributes or annotations must be provide…
Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
Yanfeng Yang, Siwei Chen, Pingping Hu +6
Probabilistic forecasting of multivariate time series is challenging due to non-stationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow…
Cognitive Chunking for Soft Prompts: Accelerating Compressor Learning via Block-wise Causal Masking
Guojie Liu, Yiqi Wang, Yanfeng Yang +4
Providing extensive context via prompting is vital for leveraging the capabilities of Large Language Models (LLMs). However, lengthy contexts significantly increase inference laten…
Fast Flow Matching based Conditional Independence Tests for Causal Discovery
Shunyu Zhao, Yanfeng Yang, Shuai Li +1
Constraint-based causal discovery methods require a large number of conditional independence (CI) tests, which severely limits their practical applicability due to high computation…
Conditional Diffusion Models Based Conditional Independence Testing
Yanfeng Yang, Shuai Li, Yingjie Zhang +4
Conditional independence (CI) testing is a fundamental task in modern statistics and machine learning. The conditional randomization test (CRT) was recently introduced to test whet…