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Xiaohong Chen

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10 papers

2 author rows

TCS Journal 2026 Journal Article

A unifying logical foundation for initial algebra semantics and induction

Xiaohong Chen
Dorel Lucanu
Grigore Roşu

Initial algebra semantics provides a generic and principled framework to study induction. In this paper, we give a complete formalization of ini- tial algebra semantics and inductive reasoning using matching logic—a small and unifying logic for formal semantics of programming languages. Specifi- cally, we define initial algebra semantics as matching logic theories and derive induction/iteration/primitive-recursion principles as formal theorems within matching logic, using its proof system. This way, we obtain, for the first time, a rigorous logical foundation for general initial algebra semantics and induc- tion, both proof-theoretically and model-theoretically. As a bonus, matching logic admits the smallest known proof checker for a logic supporting inductive proofs, of only 240 lines of code.

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I&C Journal 2026 Journal Article

Parameterized approximation schemes for fair-range clustering

Zhen Zhang
Xiaohong Chen
Limei Liu
Jie Chen
Junyu Huang
Qilong Feng

Fair-range clustering extends classical clustering formulations by associating each data point with one or more demographic labels. It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected facilities. In this paper we focus on the fair-range k-median and k-means problems in Euclidean spaces. We give ( 1 + ε ) -approximation algorithms with fixed-parameter tractable running times for both problems, parameterized by the numbers of opened facilities and demographic labels. For Euclidean metrics, these are the first parameterized approximation schemes for the problems, improving upon the previously known O(1)-approximation ratios given by Thejaswi et al. (KDD 2022), later albeit applicable to general metric spaces.

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AAAI Conference 2026 Conference Paper

RESTL: Reinforcement Learning Guided by Multi-Aspect Rewards for Signal Temporal Logic Transformation

Yue Fang
Zhi Jin
Jie An
Hongshen Chen
Xiaohong Chen
Naijun Zhan

Signal Temporal Logic (STL) is a powerful formal language for specifying real-time specifications of Cyber-Physical Systems (CPS). Transforming specifications written in natural language into STL formulas automatically has attracted increasing attention. Existing rule-based methods depend heavily on rigid pattern matching and domain-specific knowledge, limiting their generalizability and scalability. Recently, Supervised Fine-Tuning (SFT) of large language models (LLMs) has been successfully applied to transform natural language into STL. However, the lack of fine-grained supervision on atomic proposition correctness, semantic fidelity, and formula readability often leads SFT-based methods to produce formulas misaligned with the intended meaning. To address these issues, we propose RESTL, a reinforcement learning (RL)-based framework for the transformation from natural language to STL. RESTL introduces multiple independently trained reward models that provide fine-grained, multi-faceted feedback from four perspectives, i.e., atomic proposition consistency, semantic alignment, formula succinctness, and symbol matching. These reward models are trained with a curriculum learning strategy to improve their feedback accuracy, and their outputs are aggregated into a unified signal that guides the optimization of the STL generator via Proximal Policy Optimization (PPO). Experimental results demonstrate that RESTL significantly outperforms state-of-the-art methods in both automatic metrics and human evaluations.

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JBHI Journal 2026 Journal Article

Whisperization and Masked CycleGAN-Based Framework for Electrolaryngeal Speech Enhancement

Jie Zhou
Li Wang
Fengji Li
Shaochuan Zhang
Fan Fan
Tao Liu
Xiaohong Chen
Haijun Niu

Electrolarynx (EL) provides an effective approach to voice rehabilitation for patients with phonation disorder. However, due to its reliance on an external mechanical source, EL speech suffers from limited acoustic cues, leading to degraded quality and restricting the potential of subsequent modeling and enhancement. This paper proposes a novel EL speech enhancement framework that combines whisperization with Masked CycleGAN model. The whisperization step removes redundant constant excitation and mechanical noise, generating an intermediate speech form—whisper-like EL (W-EL) speech, whose acoustic and perceptual properties are closer to natural whisper. Subsequently, the Masked CycleGAN employs a frame-level masking strategy to guide the generator in reconstructing missing prosodic and linguistic features. Thus, we achieved a dual-stage enhancement of “redundancy removal” and “deficiency compensation. ” Acoustic feature analysis demonstrates that the converted W-EL speech is more similar to normal speech in terms of spectrogram, fundamental frequency (F0) values, and F0 contours, while also compensating for the missing low frequency energy below 500 Hz. Objective evaluations show significant improvements across multiple metrics. Subjective evaluations confirm that W-EL speech exhibits higher naturalness and intelligibility compared to original EL speech. Moreover, the combined “whisperization + voice conversion” framework further enhances perceptual quality. This study not only offer a novel pathway for EL speech enhancement, but also may provide valuable insights for improving other types of pathological speech.

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EAAI Journal 2026 Journal Article

X-ray/CT image registration based on triple-cycle modal unification network

Yuanxi Sun
Chuan Tang
Xin Zhong
Xiaohong Chen
Jia Zheng
Long Bai

Accurate registration between X-ray and Computed Tomography (CT) images plays a vital role in many clinical and surgical applications. However, deep learning-based two-dimensional/three-dimensional (2D/3D) registration methods often rely on digitally reconstructed radiographs generated through ray casting, where the substantial modality gap between X-ray and Digitally Reconstructed Radiograph (DRR) images can significantly reduce registration accuracy. This study proposes a triple-cycle modality unification network that introduces an intermediate bias-neutral modality to bridge the domain discrepancy between the two modalities. Both X-ray and DRR images are transformed into this unified modality, effectively minimizing modality bias and reducing information loss associated with one-way translation. Furthermore, an efficient hybrid registration network is designed by integrating the global context modeling capability of Transformer architectures with the local feature extraction strength of convolutional neural networks, supported by a multi-scale feature extraction strategy. Experimental results demonstrate that the proposed method achieves an average rotation error of approximately 0. 5° and translation errors of about 0. 5 mm in the X and Z directions and less than 1 mm in the Y direction. These findings indicate that the proposed framework achieves high-precision registration and has strong potential for practical application in computer-assisted diagnosis and image-guided interventions.

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NeurIPS Conference 2024 Conference Paper

Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

Xiaohong Chen
Canran Xiao
Yongmei Liu

Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data. However, handling non-i. i. d. (not identically and independently distributed) data and ensuring model consistency across heterogeneous environments present significant challenges. These challenges often lead to model performance degradation and increased difficulty in achieving effective communication among participant models. In this work, we propose Confusion-Resistant Federated Learning via Consistent Diffusion (CRFed), a novel framework designed to address these issues. Our approach introduces a new diffusion-based data harmonization mechanism that includes data augmentation, noise injection, and iterative denoising to ensure consistent model updates across non-i. i. d. data distributions. This mechanism aims to reduce data distribution disparities among participating nodes, enhancing the coordination and consistency of model updates. Moreover, we design a confusion-resistant strategy leveraging an indicator function and adaptive learning rate adjustment to mitigate the adverse effects of data heterogeneity and model inconsistency. Specifically, we calculate importance sampling weights based on the optimal sampling probability, which guides the selection of clients and the sampling of their data, ensuring that model updates are robust and aligned across different nodes. Extensive experiments on benchmark datasets, including MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and NIPD, demonstrate the effectiveness of CRFed in improving accuracy, convergence speed, and overall robustness in federated learning scenarios with severe data heterogeneity.

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NeurIPS Conference 2024 Conference Paper

Parameterized Approximation Schemes for Fair-Range Clustering

Zhen Zhang
Xiaohong Chen
Limei Liu
Jie Chen
Junyu Huang
Qilong Feng

Fair-range clustering extends classical clustering formulations by associating each data point with one or more demographic labels. It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected facilities. In this paper we focus on the fair-range $k$-median and $k$-means problems in Euclidean spaces. We give $(1+\varepsilon)$-approximation algorithms with fixed-parameter tractable running times for both problems, parameterized by the numbers of opened facilities and demographic labels. For Euclidean metrics, these are the first parameterized approximation schemes for the problems, improving upon the previously known $O(1)$-approximation ratios given by Thejaswi et al. (KDD 2022).

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ICRA Conference 2024 Conference Paper

Synchronized Dual-arm Rearrangement via Cooperative mTSP

Wenhao Li
Shishun Zhang
Sisi Dai
Hui Huang 0004
Ruizhen Hu
Xiaohong Chen
Kai Xu 0004

Synchronized dual-arm rearrangement is widely studied as a common scenario in industrial applications. It often faces scalability challenges due to the computational complexity of robotic arm rearrangement and the high-dimensional nature of dual-arm planning. To address these challenges, we formulated the problem as cooperative mTSP, a variant of mTSP where agents share cooperative costs, and utilized reinforcement learning for its solution. Our approach involved representing rearrangement tasks using a task state graph that captured spatial relationships and a cooperative cost matrix that provided details about action costs. Taking these representations as observations, we designed an attention-based network to effectively combine them and provide rational task scheduling. Furthermore, a cost predictor is also introduced to directly evaluate actions during both training and planning, significantly expediting the planning process. Our experimental results demonstrate that our approach outperforms existing methods in terms of both performance and planning efficiency.

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AIIM Journal 2022 Journal Article

Attribute-aware interpretation learning for thyroid ultrasound diagnosis

Ming Kong
Qing Guo
Shuowen Zhou
Mengze Li
Kun Kuang
Zhengxing Huang
Fei Wu
Xiaohong Chen

Thyroid nodule diagnosis from ultrasound images is a critical computer-aided diagnosis task. Previous works tried to imitate the doctor's diagnosis logic by considering the key attributes to improve the diagnosis performance and explaining the conclusion. However, their clinical feasibilities are still ambiguous because of the ignorance of the correlation between attribute features and global characteristics, as well as the lack of clinical effectiveness evaluation of result interpretations. Following the common logic of ultrasonic investigation, we design a novel Attribute-Aware Interpretation Learning (AAIL) model, consisting of attribute properties discovery module and attribute-global feature fusion module. Adequate result interpretation ensures reliability and transparency of diagnostic conclusions, including the visualization of attribute features and the relationship between attributes and the global feature. Extensive experiments on a practical dataset demonstrate the model's effectiveness, and an innovative human-computer collaborative experiment demonstrates the auxiliary diagnostic ability of the interpretations that can benefit professional doctors.

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ICML Conference 2022 Conference Paper

On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation

Xiaohong Chen
Zhengling Qi

We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision process with continuous states and actions. We recast the $Q$-function estimation into a special form of the nonparametric instrumental variables (NPIV) estimation problem. We first show that under one mild condition the NPIV formulation of $Q$-function estimation is well-posed in the sense of $L^2$-measure of ill-posedness with respect to the data generating distribution, bypassing a strong assumption on the discount factor $\gamma$ imposed in the recent literature for obtaining the $L^2$ convergence rates of various $Q$-function estimators. Thanks to this new well-posed property, we derive the first minimax lower bounds for the convergence rates of nonparametric estimation of $Q$-function and its derivatives in both sup-norm and $L^2$-norm, which are shown to be the same as those for the classical nonparametric regression (Stone, 1982). We then propose a sieve two-stage least squares estimator and establish its rate-optimality in both norms under some mild conditions. Our general results on the well-posedness and the minimax lower bounds are of independent interest to study not only other nonparametric estimators for $Q$-function but also efficient estimation on the value of any target policy in off-policy settings.

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