3 citations · 13 across the 34 of their papers we have counts for
26 papers · 1 filter
HART: Hybrid Attention-RWKV Time series Foundation Model
Qingwen Lin, Boyan Xu, Shifeng Xie +3
Non-autoregressive multi-step forecasting (NAF) enables time series foundation models to predict an entire future horizon in a single forward pass. Despite its strong sequence mode…
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Juntao Fang, Shifeng Xie, Ruichu Cai +6
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable rep…
SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models
Qingwen Lin, Boyan Xu, Xiao Liu +2
Delta-Rule recurrent models maintain a fixed-size state, enabling inference memory but potentially becoming unstable under extreme-context extrapolation. By tracking RWKV-7…
IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning
Zefeng Liang, Jie Qiao, Ruichu Cai +2
Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous…
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
Jie Qiao, Ruichu Cai, Zijian Li +6
Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines. Over the past decades, num…
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
Ruichu Cai, Xiaokai Huang, Wei Chen +2
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interfe…
Identification of Causal Direction under an Arbitrary Number of Latent Confounders
Wei Chen, Linjun Peng, Zhiyi Huang +4
Recovering causal structure in the presence of latent variables is an important but challenging task. While many methods have been proposed to handle it, most of them require stric…
Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants
Wei Chen, Wanyang Gu, Linjun Peng +3
Federated causal discovery aims to uncover the causal relationships between entities while protecting data privacy, which has significant importance and numerous applications in re…
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
Wei Chen, Jiahao Zhang, Haipeng Zhu +5
Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone…
Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism
Ruichu Cai, Kaitao Zheng, Junxian Huang +4
Time series imputation is one of the most challenge problems and has broad applications in various fields like health care and the Internet of Things. Existing methods mainly aim t…
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
Ruichu Cai, Haiqin Huang, Zhifang Jiang +5
Current methods for time series forecasting struggle in the online scenario, since it is difficult to preserve long-term dependency while adapting short-term changes when data are…
Long-term Causal Inference via Modeling Sequential Latent Confounding
Weilin Chen, Ruichu Cai, Yuguang Yan +2
Long-term causal inference is an important but challenging problem across various scientific domains. To solve the latent confounding problem in long-term observational studies, ex…
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
Xuexin Chen, Ruichu Cai, Kaitao Zheng +4
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has considerable real-world applications. One of the most mainstre…
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Zhengming Chen, Ruichu Cai, Feng Xie +5
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Mos…
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Zeqin Yang, Weilin Chen, Ruichu Cai +7
Long-term treatment effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions, such as no unobserved confounders o…
From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
Ruichu Cai, Zhifang Jiang, Zijian Li +6
Existing methods for multi-modal time series representation learning aim to disentangle the modality-shared and modality-specific latent variables. Although achieving notable perfo…
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng +5
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal p…
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
Weilin Chen, Ruichu Cai, Zeqin Yang +4
Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semipar…
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
Xuexin Chen, Ruichu Cai, Kaitao Zheng +4
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has a massive of real-world applications. One of the most mainstre…
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Zijian Li, Ruichu Cai, Zhenhui Yang +6
As environments evolve, temporal distribution shifts can degrade time series forecasting performance. A straightforward solution is to adapt to nonstationary changes while preservi…
Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
Xuexin Chen, Ruichu Cai, Zhengting Huang +5
We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests…
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
Ruichu Cai, Yuxuan Zhu, Jie Qiao +3
Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted \emph{adversarial examples}, which are generated through either well-conceived -n…
Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model
Jie Qiao, Zhengming Chen, Jianhua Yu +2
Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-m…
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Wei Chen, Zhiyi Huang, Ruichu Cai +2
Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identifie…