Author name cluster

Ning Gui

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.

9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Generating-Filtering-Ranking: A Three-Stage MultiModal Data Augmentation Framework Under Partial Modality Missing

Zhirui Kuai
Huan ZHang
Yang Yang
Yiping Ma
Mingjing Huang
Ning Gui
Li Kuang

Multimodal data significantly improves the performance of pretrained models, but its practical application is often limited by missing or incomplete data across modalities. There are two key challenges that existing methods of synthesizing missing data face: (1) semantic inaccuracies due to model hallucinations and (2) discrepancies in distribution preferences between generated and original data. To address these challenges, we propose a novel three-stage multimodal data augmentation framework (GFR), which Generate, Filter, and Rank missing modality data. Our framework leverages multimodal large models for diverse data generation, designs a scene graph matching-based filtering algorithm to ensure semantic consistency, and constructs a preference-aware ranking model to align the generated data with both the original distribution and task relevance. Our framework not only enhances semantic diversity and consistency in data generation but also effectively captures the implicit characteristics of the original dataset and the target model. We demonstrate the effectiveness of GFR across multiple datasets by testing different missing types and missing ratios.

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TMLR Journal 2025 Journal Article

Are Large Language Models Really Robust to Word-Level Perturbations?

Haoyu Wang
Guozheng Ma
Cong Yu
Ning Gui
Linrui Zhang
Zhiqi Huang
Suwei Ma
Yongzhe Chang

The swift advancement in the scales and capabilities of Large Language Models (LLMs) positions them as promising tools for a variety of downstream tasks. In addition to the pursuit of better performance and the avoidance of violent feedback on a certain prompt, to ensure the responsibility of the LLMs, much attention is drawn to the robustness of LLMs. However, existing evaluation methods mostly rely on traditional question answering datasets with predefined supervised labels, potentially ignoring the superior generation capabilities of contemporary LLMs. To investigate the robustness of LLMs while using their generation ability, we propose a novel rational evaluation pipeline that leverages reward models as diagnostic tools to evaluate the long conversation generated from more challenging open questions by LLMs, which we refer to as the Reward Model for Reasonable Robustness Evaluation (TREvaL). Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions, a capability not entirely encompassed by individual words or letters.Our extensive empirical experiments demonstrate that TREvaL provides an identification for the lack of robustness of nowadays LLMs.Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted, calling for more attention on the robustness during alignment process.

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ICML Conference 2025 Conference Paper

Non-stationary Diffusion For Probabilistic Time Series Forecasting

Weiwei Ye
Zhuopeng Xu
Ning Gui

Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing Denoising Diffusion Probabilistic Models (DDPMs) often fail to capture this non-stationary nature, constrained by their constant variance assumption from the additive noise model (ANM). In this paper, we innovatively utilize the Location-Scale Noise Model (LSNM) to relax the fixed uncertainty assumption of ANM. A diffusion-based probabilistic forecasting framework, termed Non-stationary Diffusion (NsDiff), is designed based on LSNM that is capable of modeling the changing pattern of uncertainty. Specifically, NsDiff combines a denoising diffusion-based conditional generative model with a pre-trained conditional mean and variance estimator, enabling adaptive endpoint distribution modeling. Furthermore, we propose an uncertainty-aware noise schedule, which dynamically adjusts the noise levels to accurately reflect the data uncertainty at each step and integrates the time-varying variances into the diffusion process. Extensive experiments conducted on nine real-world and synthetic datasets demonstrate the superior performance of NsDiff compared to existing approaches. Code is available at https: //github. com/wwy155/NsDiff.

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NeurIPS Conference 2024 Conference Paper

Frequency Adaptive Normalization For Non-stationary Time Series Forecasting

Weiwei Ye
Songgaojun Deng
Qiaosha Zou
Ning Gui

Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with certain statistical measures, e. g. , mean and variance. Although they demonstrate improved predictive accuracy, they are limited to expressing basic trends and are incapable of handling seasonal patterns. To address this limitation, this paper proposes a new instance normalization solution, called frequency adaptive normalization (FAN), which extends instance normalization in handling both dynamic trend and seasonal patterns. Specifically, we employ the Fourier transform to identify instance-wise predominant frequent components that cover most non-stationary factors. Furthermore, the discrepancy of those frequency components between inputs and outputs is explicitly modeled as a prediction task with a simple MLP model. FAN is a model-agnostic method that can be applied to arbitrary predictive backbones. We instantiate FAN on four widely used forecasting models as the backbone and evaluate their prediction performance improvements on eight benchmark datasets. FAN demonstrates significant performance advancement, achieving 7. 76\%$\sim$37. 90\% average improvements in MSE. Our code is publicly available at http: //github. com/icannotnamemyself/FAN.

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NeurIPS Conference 2024 Conference Paper

NeuralPlane: An Efficiently Parallelizable Platform for Fixed-wing Aircraft Control with Reinforcement Learning

Chuanyi Xue
Qihan Liu
Xiaoteng Ma
Yang Qi
Xinyao Qin
Yuhua Jiang
Ning Gui
Jinsheng Ren

Reinforcement learning (RL) demonstrates superior potential over traditional flight control methods for fixed-wing aircraft, particularly under extreme operational conditions. However, the high demand for training samples and the lack of efficient computation in existing simulators hinder its further application. In this paper, we introduce NeuralPlane, the first benchmark platform for large-scale parallel simulations of fixed-wing aircraft. NeuralPlane significantly boosts high-fidelity simulation via GPU-accelerated Flight Dynamics Model (FDM) computation, achieving a single-step simulation time of just 0. 2 seconds at a parallel scale of $10^{6}$, far exceeding current platforms. We also provide clear code templates, comprehensive evaluation/visualization tools and hierarchical frameworks for integrating RL and traditional control methods. We believe that NeuralPlane can accelerate the development of RL-based fixed-wing flight control and serve as a new challenging benchmark for the RL community. Our NeuralPlane is open-source and accessible at https: //github. com/xuecy22/NeuralPlane.

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NeurIPS Conference 2024 Conference Paper

Ordering-Based Causal Discovery for Linear and Nonlinear Relations

Zhuopeng Xu
Yujie Li
Cheng Liu
Ning Gui

Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, which is often not reflective of real-world datasets that contain a combination of both. This paper presents CaPS, an ordering-based causal discovery algorithm that effectively handles linear and nonlinear relations. CaPS introduces a novel identification criterion for topological ordering and incorporates the concept of "parent score" during the post-processing optimization stage. These scores quantify the strength of the average causal effect, helping to accelerate the pruning process and correct inaccurate predictions in the pruning step. Experimental results demonstrate that our proposed solutions outperform state-of-the-art baselines on synthetic data with varying ratios of linear and nonlinear relations. The results obtained from real-world data also support the competitiveness of CaPS. Code and datasets are available at https: //github. com/E2real/CaPS.

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AAAI Conference 2023 Conference Paper

Data Imputation with Iterative Graph Reconstruction

Jiajun Zhong
Ning Gui
Weiwei Ye

Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based data imputation solutions show their structure learning potentials by translating tabular data as bipartite graphs. However, due to a lack of relations between samples, they treat all samples equally which is against one important observation: ``similar sample should give more information about missing values." This paper presents a novel Iterative graph Generation and Reconstruction framework for Missing data imputation(IGRM). Instead of treating all samples equally, we introduce the concept: ``friend networks" to represent different relations among samples. To generate an accurate friend network with missing data, an end-to-end friend network reconstruction solution is designed to allow for continuous friend network optimization during imputation learning. The representation of the optimized friend network, in turn, is used to further optimize the data imputation process with differentiated message passing. Experiment results on eight benchmark datasets show that IGRM yields 39.13% lower mean absolute error compared with nine baselines and 9.04% lower than the second-best. Our code is available at https://github.com/G-AILab/IGRM.

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AAAI Conference 2019 Conference Paper

AFS: An Attention-Based Mechanism for Supervised Feature Selection

Ning Gui
Danni Ge
Ziyin Hu

As an effective data preprocessing step, feature selection has shown its effectiveness to prepare high-dimensional data for many machine learning tasks. The proliferation of high di-mension and huge volume big data, however, has brought major challenges, e. g. computation complexity and stability on noisy data, upon existing feature-selection techniques. This paper introduces a novel neural network-based feature selection architecture, dubbed Attention-based Feature Selec-tion (AFS). AFS consists of two detachable modules: an at-tention module for feature weight generation and a learning module for the problem modeling. The attention module for-mulates correlation problem among features and supervision target into a binary classification problem, supported by a shallow attention net for each feature. Feature weights are generated based on the distribution of respective feature selec-tion patterns adjusted by backpropagation during the training process. The detachable structure allows existing off-the-shelf models to be directly reused, which allows for much less training time, demands for the training data and requirements for expertise. A hybrid initialization method is also introduced to boost the selection accuracy for datasets without enough samples for feature weight generation. Experimental results show that AFS achieves the best accuracy and stability in comparison to several state-of-art feature selection algorithms upon both MNIST, noisy MNIST and several datasets with small samples.

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EAAI Journal 2014 Journal Article

ATALK: A decentralized agent platform for engineering open and dynamic organizations

Ning Gui
Vincenzo De Florio
Tom Holvoet

In recent years the concept of “organization” has been gradually accepted as a suitable abstraction for the development of open multi-agent systems with dynamic agents. By modelling interactions based on roles, this abstraction allows an organization to be modelled independently of its participating agents so as to support agent dynamicity. A natural requirement from the abstraction is that agents need to be “organization aware”—that is they must be able to inquire, import, and execute roles from the organization. However, how to adequately support those processes in a decentralized and dynamic environment is still an open issue. This paper addresses this problem by presenting ATALK, a novel decentralized agent middleware that fulfills the above requirements. A role component model is designed to facilitate role deployment, reflection and uniform role interaction. In order to support role dynamicity, a compositional agent architecture allowing dynamic role integration and enactment is proposed. Moreover, ATALK allows agents to hand over their role to other agents of a same organization with their run-time states. Thus, agents can dynamically relinquish their roles without jeopardizing the normal execution of the organization. This paper also evaluates our design and current prototypic implementation through both qualitative analysis and simulations. Our results show that ATALK achieves remarkable organization reconfigurability with little additional overhead.

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