124 citations · 144 across the 19 of their papers we have counts for
20 papers
TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Zijian Li, Xiangchen Song, Gongxu Luo +7
Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with overall performance largely…
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…
An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning
Ruichu Cai, Xi Chen, Jie Qiao +5
Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal stat…
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Weilin Chen, Ruichu Cai, Jie Qiao +2
Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfounded…
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
Feng Zhou, Quyu Kong, Jie Qiao +3
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing h…
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…
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…
Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Jie Qiao, Yu Xiang, Zhengming Chen +2
Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientifi…
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…
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
Yuequn Liu, Ruichu Cai, Wei Chen +5
Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences a…
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Jie Qiao, Ruichu Cai, Siyu Wu +3
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes p…
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
Ruichu Cai, Yuxuan Zhu, Xuexin Chen +4
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessar…
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
Ruichu Cai, Fengzhu Wu, Zijian Li +4
The recommendation system, relying on historical observational data to model the complex relationships among the users and items, has achieved great success in real-world applicati…
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Weilin Chen, Jie Qiao, Ruichu Cai +1
Causal discovery from observational data is an important but challenging task in many scientific fields. Recently, a method with non-combinatorial directed acyclic constraint, call…
THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences
Ruichu Cai, Siyu Wu, Jie Qiao +3
Learning causal structure among event types on multi-type event sequences is an important but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly…
Learning Disentangled Semantic Representation for Domain Adaptation
Ruichu Cai, Zijian Li, Pengfei Wei +3
Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space…
Disentanglement Challenge: From Regularization to Reconstruction
Jie Qiao, Zijian Li, Boyan Xu +2
The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition using a new real world disentanglement dataset (Gondal e…
Causal Discovery with Cascade Nonlinear Additive Noise Models
Ruichu Cai, Jie Qiao, Kun Zhang +2
Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have bee…