b'Ruichu Cai'

research

∙ 08/08/2023

Generalization bound for estimating causal effects from observational network data

Estimating causal effects from observational network data is a significa...

0 Ruichu Cai, et al. ∙

research

∙ 06/25/2023

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences

Learning Granger causality from event sequences is a challenging but ess...

0 Ruichu Cai, et al. ∙

research

∙ 05/10/2023

Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences

Learning causal structure among event types from discrete-time event seq...

0 Jie Qiao, et al. ∙

research

∙ 02/10/2023

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequent...

0 Yan Zeng, et al. ∙

research

∙ 12/14/2022

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

Explainability of Graph Neural Networks (GNNs) is critical to various GN...

0 Ruichu Cai, et al. ∙

research

∙ 08/09/2022

Long-term Causal Effects Estimation via Latent Surrogates Representation Learning

Estimating long-term causal effects based on short-term surrogates is a ...

0 Ruichu Cai, et al. ∙

research

∙ 05/07/2022

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance

Domain adaptation on time-series data is often encountered in the indust...

0 Zijian Li, et al. ∙

research

∙ 01/13/2022

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

The recommendation system, relying on historical observational data to m...

0 Ruichu Cai, et al. ∙

research

∙ 12/30/2021

Motif Graph Neural Network

Graphs can model complicated interactions between entities, which natura...

0 Xuexin Chen, et al. ∙

research

∙ 11/18/2021

CCSL: A Causal Structure Learning Method from Multiple Unknown Environments

Most existing causal structure learning methods require data to be indep...

0 Wei Chen, et al. ∙

research

∙ 11/14/2021

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Sequential recommendation aims to choose the most suitable items for a u...

0 Zijian Li, et al. ∙

research

∙ 11/05/2021

Transferable Time-Series Forecasting under Causal Conditional Shift

This paper focuses on the problem of semi-supervised domain adaptation f...

0 Zijian Li, et al. ∙

research

∙ 11/01/2021

SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL

The Text-to-SQL task, aiming to translate the natural language of the qu...

0 Ruichu Cai, et al. ∙

research

∙ 06/14/2021

Graph Domain Adaptation: A Generative View

Recent years have witnessed tremendous interest in deep learning on grap...

0 Ruichu Cai, et al. ∙

research

∙ 06/05/2021

On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization

Causal discovery from observational data is an important but challenging...

0 Ruichu Cai, et al. ∙

research

∙ 05/23/2021

THP: Topological Hawkes Processes for Learning Granger Causality on Event Sequences

Learning Granger causality among event types on multi-type event sequenc...

0 Ruichu Cai, et al. ∙

research

∙ 03/26/2021

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders

We consider the problem of estimating a particular type of linear non-Ga...

0 Wei Chen, et al. ∙

research

∙ 12/22/2020

Learning Disentangled Semantic Representation for Domain Adaptation

Domain adaptation is an important but challenging task. Most of the exis...

0 Ruichu Cai, et al. ∙

research

∙ 12/22/2020

Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition

Named entity recognition (NER) for identifying proper nouns in unstructu...

0 Zhifeng Hao, et al. ∙

research

∙ 12/22/2020

Time Series Domain Adaptation via Sparse Associative Structure Alignment

Domain adaptation on time series data is an important but challenging ta...

0 Ruichu Cai, et al. ∙

research

∙ 10/10/2020

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs

Causal discovery aims to recover causal structures or models underlying ...

4 Feng Xie, et al. ∙

research

∙ 09/19/2020

Causal Discovery with Multi-Domain LiNGAM for Latent Factors

Discovering causal structures among latent factors from observed data is...

0 Yan Zeng, et al. ∙

research

∙ 05/06/2020

TAG : Type Auxiliary Guiding for Code Comment Generation

Existing leading code comment generation approaches with the structure-t...

0 Ruichu Cai, et al. ∙

research

∙ 11/30/2019

Disentanglement Challenge: From Regularization to Reconstruction

The challenge of learning disentangled representation has recently attra...

0 Jie Qiao, et al. ∙

research

∙ 10/13/2019

Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

Data-driven models are becoming essential parts in modern mechanical sys...

0 Zijian Li, et al. ∙

research

∙ 05/23/2019

Causal Discovery with Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from obs...

2 Ruichu Cai, et al. ∙

research

∙ 11/16/2017

An Encoder-Decoder Framework Translating Natural Language to Database Queries

Machine translation is going through a radical revolution, driven by the...

0 Ruichu Cai, et al. ∙

research

∙ 07/05/2017

SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee

Causation discovery without manipulation is considered a crucial problem...

0 Ruichu Cai, et al. ∙

research

∙ 09/07/2016

Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing

Traditional dehazing techniques, as a well studied topic in image proces...

0 Meihua Wang, et al. ∙

Ruichu Cai

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