8 citations · 12 across the 29 of their papers we have counts for
16 papers · 1 filter
Transportable Causal Effect Estimation across Networks under Interference
Xiaojing Du, Jiuyong Li, Lin Liu +3
Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention…
Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
Wentao Gao, Jiuyong Li, Lin Liu +4
Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate fore…
Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting
Wentao Gao, Jiuyong Li, Lin Liu +4
Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert climatolog…
uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
Sha Lu, Jixue Liu, Stefan Peters +4
Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cu…
Learning Fair Graph Representations with Multi-view Information Bottleneck
Chuxun Liu, Debo Cheng, Qingfeng Chen +3
Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory at…
From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
Wentao Gao, Jiuyong Li, Lin Liu +6
Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Infla…
Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
Xiaojing Du, Jiuyong Li, Lin Liu +2
Estimating peer causal effects within complex real-world networks such as social networks is challenging, primarily due to simultaneous feedback between peers and unobserved confou…
Disentangled Representation Learning for Causal Inference with Instruments
Debo Cheng, Jiuyong Li, Lin Liu +4
Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this chal…
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes com…
Linking Model Intervention to Causal Interpretation in Model Explanation
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the f…
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
Wentao Gao, Ziqi Xu, Jiuyong Li +6
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting be…
Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift
Oscar Blessed Deho, Michael Bewong, Selasi Kwashie +4
Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of…
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
Yang Xie, Ziqi Xu, Debo Cheng +4
Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most o…
Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the pres…
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
Debo Cheng, Yang Xie, Ziqi Xu +5
In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observ…
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Sha Lu, Lin Liu, Kui Yu +3
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a…