Author name cluster

Xin Ding

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5 papers

2 author rows

ICLR Conference 2025 Conference Paper

CBQ: Cross-Block Quantization for Large Language Models

Xin Ding
Xiaoyu Liu 0006
Zhijun Tu
Yun Zhang
Wei Li 0002
Jie Hu 0021
Hanting Chen
Yehui Tang 0001

Post-training quantization (PTQ) has played a pivotal role in compressing large language models (LLMs) at ultra-low costs. Although current PTQ methods have achieved promising results by addressing outliers and employing layer- or block-wise loss optimization techniques, they still suffer from significant performance degradation at ultra-low bits precision. To dissect this issue, we conducted an in-depth analysis of quantization errors specific to LLMs and surprisingly discovered that, unlike traditional sources of quantization errors, the growing number of model parameters, combined with the reduction in quantization bits, intensifies inter-layer and intra-layer dependencies, which severely impact quantization accuracy. This finding highlights a critical challenge in quantizing LLMs. To address this, we propose CBQ, a cross-block reconstruction-based PTQ method for LLMs. CBQ leverages a cross-block dependency to establish long-range dependencies across multiple blocks and integrates an adaptive LoRA-Rounding technique to manage intra-layer dependencies. To further enhance performance, CBQ incorporates a coarse-to-fine pre-processing mechanism for processing weights and activations. Extensive experiments show that CBQ achieves superior low-bit quantization (W4A4, W4A8, W2A16) and outperforms existing state-of-the-art methods across various LLMs and datasets. Notably, CBQ only takes 4.3 hours to quantize a weight-only quantization of a 4-bit LLAMA1-65B model, achieving a commendable trade off between performance and efficiency.

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ICML Conference 2025 Conference Paper

TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration

Cheng Xin
Fan Xu
Xin Ding
Jie Gao 0001
Jiaxin Ding 0001

Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields, yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently, intrinsic interpretable GNNs have been studied to provide insights into model predictions by identifying rationale substructures in graphs. However, existing methods face challenges when the underlying rationale subgraphs are complex and varied. In this work, we propose TopInG: Topologically Interpretable Graph Learning, a novel topological framework that leverages persistent homology to identify persistent rationale subgraphs. TopInG employs a rationale filtration learning approach to model an autoregressive generating process of rationale subgraphs, and introduces a self-adjusted topological constraint, termed topological discrepancy, to enforce a persistent topological distinction between rationale subgraphs and irrelevant counterparts. We provide theoretical guarantees that our loss function is uniquely optimized by the ground truth under specific conditions. Extensive experiments demonstrate TopInG’s effectiveness in tackling key challenges, such as handling variform rationale subgraphs, balancing predictive performance with interpretability, and mitigating spurious correlations. Results show that our approach improves upon state-of-the-art methods on both predictive accuracy and interpretation quality.

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IROS Conference 2024 Conference Paper

Driving Style Alignment for LLM-powered Driver Agent

Ruoxuan Yang
Xinyue Zhang
Anais Fernandez-Laaksonen
Xin Ding
Jiangtao Gong

Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities. However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human driving behaviors. To address this research gap, we propose a multi-alignment framework designed to align driver agents with human driving styles through demonstrations and feedback. Notably, we construct a natural language dataset of human driver behaviors through naturalistic driving experiments and post-driving interviews, offering high-quality human demonstrations for LLM alignment. The framework’s effectiveness is validated through simulation experiments in the CARLA urban traffic simulator and further corroborated by human evaluations. Our research offers valuable insights into designing driving agents with diverse driving styles. The implementation of the framework 1 and details of the dataset 2 can be found at the link.

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AAAI Conference 2024 Conference Paper

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks

Xin Ding
Yongwei Wang
Zuheng Xu

Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can produce subpar fake images due to limited training data. Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling. We present a novel NDA approach called Dual-NDA specifically tailored for CcGANs to address this problem. Dual-NDA employs two types of negative samples: visually unrealistic images generated from a pre-trained CcGAN and label-inconsistent images created by manipulating real images' labels. Leveraging these negative samples, we introduce a novel discriminator objective alongside a modified CcGAN training algorithm. Empirical analysis on UTKFace and Steering Angle reveals that Dual-NDA consistently enhances the visual fidelity and label consistency of fake images generated by CcGANs, exhibiting a substantial performance gain over the vanilla NDA. Moreover, by applying Dual-NDA, CcGANs demonstrate a remarkable advancement beyond the capabilities of state-of-the-art conditional GANs and diffusion models, establishing a new pinnacle of performance. Our codes can be found at https://github.com/UBCDingXin/Dual-NDA.

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EAAI Journal 2023 Journal Article

A survey on the mechanism and countermeasures of low-frequency swaying of high-speed trains caused by aerodynamic loads

Chao Chang
Xin Ding
Zhuang Sun
Yizheng Yu
Lei Zhang

The carbody abnormal vibration has significant impacts on the comfort and safety of high-speed train. Field measurements were conducted to study the low-frequency swaying of the carbody on a high-speed train operating on a railway line. According to the test results, the tail vehicle of the train swayed laterally in various places along the line. The lateral stability index of vehicle clearly exceeded the limit value when the carbody swayed. The predominant frequency of the lateral acceleration of the carbody was between 1. 4 and 1. 5 Hz. Through the simulation of computational fluid dynamics, it is confirmed that the yaw moment and lift force of the tail vehicle are greater than those of the middle and the head vehicles. Furthermore, the dynamics simulation show that aerodynamics disturbances may be the intensified cause of the abnormal swaying of the tail vehicle. Therefore, the study proposes the installation of inter-vehicle dampers. and optimizes the damping values of dampers based on GA-BP optimization algorithm, so as to weaken the abnormal swaying motion. The relevant simulation results offer a solution to address the abnormal swaying phenomenon in practical situations.

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