6 papers
TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
Shicheng Fan, Kun Zhang, Lu Cheng
Temporal causal representation learning methods assume that causal mechanisms switch instantaneously between discrete domains, yet real-world systems often exhibit continuous mecha…
Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
Yuqin Yang, Mohamed Nafea, Negar Kiyavash +2
The presence of unobserved common causes and measurement error poses two major obstacles to causal structure learning, since ignoring either source of complexity can induce spuriou…
Bias mitigation in graph diffusion models
Meng Yu, Kun Zhan
Most existing graph diffusion models have significant bias problems. We observe that the forward diffusion's maximum perturbation distribution in most models deviates from the stan…
Reliable Real-Time Value at Risk Estimation via Quantile Regression Forest with Conformal Calibration
Du-Yi Wang, Guo Liang, Kun Zhang +1
Rapidly evolving market conditions call for real-time risk monitoring, but its online estimation remains challenging. In this paper, we study the online estimation of one of the mo…
Diversified Scaling Inference in Time Series Foundation Models
Ruijin Hua, Zichuan Liu, Kun Zhang +1
The advancement of Time Series Foundation Models (TSFMs) has been driven primarily by large-scale pre-training, but inference-time compute potential remains largely untapped. This…
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
Dingling Yao, Shimeng Huang, Riccardo Cadei +2
Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and high-dimensionality of real-wo…