1 citations · 2 across the 6 of their papers we have counts for
6 papers
Ability Transfer and Recovery via Modularized Parameters Localization
Songyao Jin, Kun Zhou, Wenqi Li +2
Large language models can be continually pre-trained or fine-tuned to improve performance in specific domains, languages, or skills, but this specialization often degrades other ca…
Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
Songyao Jin, Biwei Huang
Multivariate Hawkes process provides a powerful framework for modeling temporal dependencies and event-driven interactions in complex systems. While existing methods primarily focu…
Causal-Copilot: An Autonomous Causal Analysis Agent
Xinyue Wang, Kun Zhou, Wenyi Wu +10
Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algo…
On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Biwei Huang +5
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, we examine the paramet…
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Xinshuai Dong, Haoyue Dai, Yewen Fan +3
Financial data is generally time series in essence and thus suffers from three fundamental issues: the mismatch in time resolution, the time-varying property of the distribution -…
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong, Biwei Huang, Ignavier Ng +6
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a no…