Author name cluster

Wentao Gao

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Deep Extreme Transformer: Tackling Zero-Inflated Time Series for Precipitation Prediction

Wentao Gao
Xiongren Chen
Xiaojing Du
Wenjun Yu
Andres Mauricio Cifuentes Bernal
Ziqi Xu

Rainfall forecasting presents a dual challenge: extreme zero inflation, where dry days dominate and obscure meaningful precipitation patterns, and pronounced nonstationarity, where climate dynamics evolve across time and regimes. We propose the Deep Extreme Transformer (DET), a principled architecture that integrates statistical distribution mod- eling with neural sequence learning to address both issues simultaneously. DET augments the Transformer with a Tweedie distribution output head that unifies discrete zeros and continuous intensities, a fixed shared-weight mech- anism that emphasizes rare but critical events in both attention and loss computation, and a Gaussian perturbation strat- egy that enhances learning stability without violating physical constraints. DET further incorporates nonstationary attention to adapt to evolving rainfall regimes. Extensive experiments on multi-decadal South Australian climate data demonstrate that DET consistently outperforms existing deep learning and statistical models across forecasting horizons. Our method provides an effective and generalizable framework for zero- inflated, shift-prone time series, bridging statistical rigor with deep temporal modeling in a unified and scalable design.

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IJCAI Conference 2025 Conference Paper

Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction

Wentao Gao
Jiuyong Li
Debo Cheng
Lin Liu
Jixue Liu
Thuc Le
Xiaojing Du
Xiongren Chen

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

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AAAI Conference 2025 Conference Paper

Diffusion Models for Attribution

Xiongren Chen
Jiuyong Li
Jixue Liu
Lin Liu
Stefan Peters
Thuc Duy Le
Wentao Gao
Xiaojing Du

In high-stakes domains such as healthcare, finance, and law, the need for explainable AI is critical. Traditional methods for generating attribution maps, including white-box approaches relying on gradients and black-box techniques that perturb inputs, face challenges like gradient vanishing, blurred attributions, and computational inefficiencies. To overcome these limitations, we introduce a novel approach that leverages diffusion models within the framework of Information Bottleneck (IB) theory. By utilizing the Gaussian noise from diffusion models, we connect the information bottleneck with the Minimum Mean Squared Error (MMSE) from classical information theory, enabling precise calculation of mutual information. This connection leads to a new loss function that minimizes the Signal-to-Noise Ratio (SNR), facilitating efficient optimization and producing high-resolution, pixel-level attribution maps. Our method achieves greater clarity and accuracy in attributions than existing techniques, requiring significantly fewer pixel values to reach the necessary predictive confidence. This work demonstrates the power of diffusion models in advancing explainable AI, particularly in identifying critical input features with high precision.

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ECAI Conference 2025 Conference Paper

From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction

Wentao Gao
Jiuyong Li
Lin Liu 0003
Thuc Duy Le
Xiongren Chen
Xiaojing Du
Jixue Liu
Yanchang Zhao

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 Inflation Diffusion Framework (ZIDF), which integrates Gaussian perturbation for smoothing zero-inflated distributions, Transformer-based prediction for capturing temporal patterns, and diffusion-based denoising to restore the original data structure. In our experiments, we use observational precipitation data collected from South Australia along with synthetically generated zero-inflated data. Results show that ZIDF demonstrates significant performance improvements over multiple state-of-the-art precipitation forecasting models, achieving up to 56. 7% reduction in MSE and 21. 1% reduction in MAE relative to the baseline Non-stationary Transformer. These findings highlight ZIDF’s ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge.

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ECAI Conference 2025 Conference Paper

PCFNet: Enhancing Time Series Forecasting Through Preserving Constant Frequency

Wenjun Yu
Wen Li
Wentao Gao
Wangyu Wu
Shouguo Du
Jiyanglin Li

Long-term time series forecasting has been widely applied in finance, traffic, and other domains. The stable periodic patterns serve as the foundation for conducting long-term forecasting. However, real-world time series often consist of multi-periodic components and trend components, which poses a significant challenge to time series prediction. In this paper, we introduce PCFNet, a simple yet effective time series forecasting model, which enhances time series forecasting by preserving the constant frequency components that represent the multi-periodicity of time series during the forecasting process. Specifically, PCFNet adaptively identifies the constant frequency components through a simple gated network. Then, the residual frequency components are predicted via a single layer of complex-valued linear layer. Finally, the residual frequency components are added to the constant frequency components to obtain the final outcome. Extensive experimental results across multiple real-world time series datasets demonstrate that PCFNet achieves state-of-the-art performance as a simple architecture.

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ICML Conference 2025 Conference Paper

Telling Peer Direct Effects from Indirect Effects in Observational Network Data

Xiaojing Du
Jiuyong Li
Debo Cheng
Lin Liu 0003
Wentao Gao
Xiongren Chen
Ziqi Xu 0001

Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Some algorithms have been proposed to estimate causal effects involving network data, particularly peer effects, but they often fail to tell apart diverse peer effects. To address this issue, we propose a general setting which considers both peer direct effects and peer indirect effects, and the effect of an individual’s own treatment, and provide the identification conditions of these causal effects. To differentiate these effects, we leverage causal mediation analysis and tailor it specifically for network data. Furthermore, given the inherent challenges of accurately estimating effects in networked environments, we propose to incorporate attention mechanisms to capture the varying influences of different neighbors and to explore high-order neighbor effects using multi-layer graph neural networks (GNNs). Additionally, we employ the Hilbert-Schmidt Independence Criterion (HSIC) to further enhance the model’s robustness and accuracy. Extensive experiments on two semi-synthetic datasets derived from real-world networks and on a dataset from a recommendation system confirm the effectiveness of our approach. Our findings have the potential to improve intervention strategies in networked systems, particularly in social networks and public health.

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AAAI Conference 2024 Conference Paper

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders

Debo Cheng
Ziqi Xu
Jiuyong Li
Lin Liu
Jixue Liu
Wentao Gao
Thuc Duy Le

Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders. Instrumental variable (IV) is a powerful tool for addressing the latent confounders issue, but the traditional IV technique cannot deal with latent time-dependent confounders in longitudinal studies. In this work, we propose a novel Time-dependent Instrumental Factor Model (TIFM) for time-varying causal effect estimation from data with latent time-dependent confounders. At each time-step, the proposed TIFM method employs the Recurrent Neural Network (RNN) architecture to infer latent IV, and then uses the inferred latent IV factor for addressing the confounding bias caused by the latent time-dependent confounders. We provide a theoretical analysis for the proposed TIFM method regarding causal effect estimation in longitudinal data. Extensive evaluation with synthetic datasets demonstrates the effectiveness of TIFM in addressing causal effect estimation over time. We further apply TIFM to a climate dataset to showcase the potential of the proposed method in tackling real-world problems.

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IROS Conference 2023 Conference Paper

A Unified Trajectory Generation Algorithm for Dynamic Dexterous Manipulation

Cheng Zhou
Wentao Gao
Weifeng Lu
Yanbo Long
Sicheng Yang
Longfei Zhao
Bidan Huang
Yu Zheng 0001

This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, separating, colliding, and grasping. Each manipulation primitive is formulate as a free-terminal optimal control problem (OCP), aimed at computing the optimal pose (position and orientation) trajectories of the object and the robot subject to the pose and force linkage constraints between them and the expected force maintenance at contact. A single-arm regrasping task and a dual-arm dynamic handover task are conducted to demonstrate the effectiveness of the proposed algorithm.

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