Author name cluster

Ziqi Xu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

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.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models

Vy Nguyen
Ziqi Xu
Jeffrey Chan
Estrid He
Feng Xia
Xiuzhen Zhang

Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or feedback, which limits their ability to prevent unreliable responses in advance. In this paper, we introduce Aspect-Based Causal Abstention (ABCA), a new framework that enables early abstention by analysing the internal diversity of LLM knowledge through causal inference. This diversity reflects the multifaceted nature of parametric knowledge acquired from various sources, representing diverse aspects such as disciplines, legal contexts, or temporal frames. ABCA estimates causal effects conditioned on these aspects to assess the reliability of knowledge relevant to a given query. Based on these estimates, we enable two types of abstention: Type-1, where aspect effects are inconsistent (knowledge conflict), and Type-2, where aspect effects consistently support abstention (knowledge insufficiency). Experiments on standard benchmarks demonstrate that ABCA improves abstention reliability, achieves state-of-the-art performance, and enhances the interpretability of abstention decisions.

PDF Details DOI

AAAI Conference 2026 Conference Paper

STEAMROLLER: A Multi-Agent System for Inclusive Automatic Speech Recognition for People Who Stutter

Ziqi Xu
Yi Liu
Yuekang Li
Ling Shi
Kailong Wang
Yongxin Zhao

People who stutter (PWS) face systemic exclusion in today’s voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depends on fluent speech. Current automatic speech recognition (ASR) systems, trained predominantly on fluent speech, fail to serve millions of PWS worldwide. We present STEAMROLLER, a real time system that transforms stuttered speech into fluent output through a novel multi-stage, multi-agent AI pipeline. Our approach addresses three critical technical challenges: (1) the difficulty of direct speech to speech conversion for disfluent input, (2) semantic distortions introduced during ASR transcription of stuttered speech, and (3) latency constraints for real time communication. STEAMROLLER employs a three stage architecture comprising ASR transcription, multi-agent text repair, and speech synthesis, where our core innovation lies in a collaborative multi-agent framework that iteratively refines transcripts while preserving semantic intent. Experiments on the FluencyBank dataset and a user study demonstrates clear word error rate (WER) reduction and strong user satisfaction. Beyond immediate accessibility benefits, fine tuning ASR on STEAMROLLER repaired speech further yields additional WER improvements, creating a pathway toward inclusive AI ecosystems.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Synthetic Forgetting Without Access: A Few-Shot Zero-Glance Framework for Machine Unlearning

Qipeng Song
Nan Yang
Ziqi Xu
Yue Li
WEI SHAO
Feng Xia

Machine unlearning aims to eliminate the influence of specific data from trained models to ensure privacy compliance. However, most existing methods assume full access to the original training dataset, which is often impractical. We address a more realistic yet challenging setting: few-shot zero-glance, where only a small subset of the retained data is available and the forget set is entirely inaccessible. We introduce GFOES, a novel framework comprising a Generative Feedback Network (GFN) and a two-phase fine-tuning procedure. GFN synthesises Optimal Erasure Samples (OES), which induce high loss on target classes, enabling the model to forget class-specific knowledge without access to the original forget data, while preserving performance on retained classes. The two-phase fine-tuning procedure enables aggressive forgetting in the first phase, followed by utility restoration in the second. Experiments on three image classification datasets demonstrate that GFOES achieves effective forgetting at both logit and representation levels, while maintaining strong performance using only 5% of the original data. Our framework offers a practical and scalable solution for privacy-preserving machine learning under data-constrained conditions.

PDF Details DOI

IROS Conference 2025 Conference Paper

Throwing Planning Diffusion: A Solution to Learning and Planning of Robotic Throwing

Ziqi Xu
Haodu Li
Lihao Liu
Jun Liu
Xuechao Duan

Dynamic manipulation enables efficient interaction tasks, such as throwing, which rely on finding one or more high-quality trajectories from the initial state to the goal state. While model-free learning methods have been used to acquire efficient robot manipulation configurations, traditional planning algorithms often struggle with multi-task specifications, high-dimensional, and multi-modal trajectory data. Prior generative model-based approaches, have made significant progress in the field of motion planning. Diffusion models, as an emerging generative model, have been widely applied to planning tasks in various environments and have gained attention for their ability in encoding multidimensional and multimodal trajectories. Here we propose our method that combines the diffusion model and model-free throwing methods. Specifically, we use a backward reachable tube to search for throwing configurations, and sample from posterior trajectory distribution conditioned on the throwing configurations. Several trajectory optimization methods are used to ensure the generation of effective throwing trajectories. Experimental results show that our method is effective in generating feasible, smooth, and collision-free throwing trajectories in both simulated and real-world tasks. Additionally, different trajectories are provided to enhance the multimodality of the throwing task.

Details

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.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Causal Inference with Conditional Instruments Using Deep Generative Models

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

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Disentangled Representation for Causal Mediation Analysis

Ziqi Xu
Debo Cheng
Jiuyong Li
Jixue Liu
Lin Liu
Ke Wang

Estimating direct and indirect causal effects from observational data is crucial to understanding the causal mechanisms and predicting the behaviour under different interventions. Causal mediation analysis is a method that is often used to reveal direct and indirect effects. Deep learning shows promise in mediation analysis, but the current methods only assume latent confounders that affect treatment, mediator and outcome simultaneously, and fail to identify different types of latent confounders (e.g., confounders that only affect the mediator or outcome). Furthermore, current methods are based on the sequential ignorability assumption, which is not feasible for dealing with multiple types of latent confounders. This work aims to circumvent the sequential ignorability assumption and applies the piecemeal deconfounding assumption as an alternative. We propose the Disentangled Mediation Analysis Variational AutoEncoder (DMAVAE), which disentangles the representations of latent confounders into three types to accurately estimate the natural direct effect, natural indirect effect and total effect. Experimental results show that the proposed method outperforms existing methods and has strong generalisation ability. We further apply the method to a real-world dataset to show its potential application.

PDF Details DOI