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Ling Zhang

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13 papers
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13

AAAI Conference 2026 Conference Paper

Fairness-Aware Design for Contextual Experiments: Guaranteeing Reliability and Equity in Heterogeneous Subgroups

  • Guangyan Gan
  • Ling Zhang
  • Yanhua Cheng
  • Yongxiang Tang
  • kaiyuan Li
  • Xialong Liu
  • Peng Jiang

Experimental design is critical for evidence-based decision-making in healthcare, marketing, and public policy. However, designing efficient experiments across heterogeneous subgroups presents significant challenges. Existing methods often optimize for statistical power or overall sample efficiency, overlooking crucial fairness considerations across these different subgroups. To address this gap, we introduce a Fairness-Aware Contextual Track-and-Stop Design (F-CTSD) algorithm. The proposed F-CTSD algorithm provides statistical guarantees on subgroup fairness while minimizing required sample sizes. We quantify the fairness-efficiency trade-off and derive the sample complexity bound for the proposed F-CTSD algorithm under its fairness constraints. We further theoretically prove that the proposed F-CTSD algorithm consistently produces accurate treatment effect estimates even under fairness requirements, enhancing statistical reliability. Numerical experiments show that the proposed F-CTSD algorithm outperforms existing methods, achieving higher sample efficiency while reducing subgroup fairness violations by 4.95%.

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JBHI Journal 2026 Journal Article

Interictal Epileptiform Discharge Detection Using Dual-Domain Features and GAN

  • Wenhao Rao
  • Jiayang Guo
  • Chunran Zhu
  • Meiyan Xu
  • Naian Xiao
  • Yijie Pan
  • Ling Zhang
  • Xiaowen Ye

Interictal Epileptiform Discharge is essential for identifying epilepsy. However, the unpredictable and non-stationary nature of electroencephalogram (EEG) patterns poses considerable challenges for reliable identification. Manual interpretation of EEG is subjective and time-consuming. With advancements in machine learning and deep learning, computer-aided approaches for automated IED detection have been rapidly developed. The state-of-the-art convolutional neural network (CNN)-based methods have shown promising results but struggle to capture long-term dependencies in time-series data. In contrast, Transformer excels at modeling sequential information through self-attention mechanisms, overcoming the CNN limitations. This study proposes an IED Detector (IEDD) that integrates convolutional layers and a Transformer to detect IEDs. The IEDD initially employs convolutional layers to extract local features of IEDs, followed by a Transformer to model long-term dependencies. To further extract spatial features, EEG data are represented as a three-dimensional tensor with embedded channel topology, where a CNN captures spatial features at each sampling point and a Long Short-Term Memory (LSTM) network models their temporal evolution. Additionally, due to the scarcity of IED data, a novel Transformer-based Generative Adversarial Network (GAN) is developed to augment the IED dataset. Experimental results show the proposed approach achieves an average accuracy of 96. 11% on the augmented Dataset 1 and 95. 25% on Dataset 2 for binary classification, with an average sensitivity of 87. 26% and precision of 89. 96% for multi-label classification. These findings provide valuable insights into advancing deep learning and Transformer-based approaches for automated IED detection.

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NeurIPS Conference 2025 Conference Paper

NaDRO: Leveraging Dual-Reward Strategies for LLMs Training on Noisy Data

  • Haolong Qian
  • Xianliang Yang
  • Ling Zhang
  • Lei Song
  • Jiang Bian
  • Chun Yuan

Group Relative Policy Optimization (GRPO) fine-tuning has demonstrated significant enhancements in reasoning tasks. However, it often relies on high quality labeled dataset, which is typically difficult to obtain. To address this challenge, we introduce \textbf{N}oise-\textbf{A}ware \textbf{D}ual-\textbf{R}eward \textbf{O}ptimization (\textbf{NaDRO}) to effectively enhances the training of Large Language Models (LLMs) under noisy or ambiguous supervision. NaDRO operates through two key components: \textbf{(1) Preference-based Outcome Reward (POR)}, which makes a principled bias-variance tradeoff, reducing training variance by learning from robust preference rankings instead of overfitting to single-best estimates; and \textbf{(2) Context Perception Reward (CPR) mechanism}, which ensures that LLMs conduct necessary qualitative assessment of the current problem state to foster deeper situational understanding prior to decision-making. To validate our approach in a realistic decision-making testbed, we model classic combinatorial optimization problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) as Markov Decision Processes, generating training data via cost-limited exploration. Our results demonstrate that the fine-tuned Qwen 7B and Llama 3. 1-8B models achieve statistically robust performance, significantly outperforming leading LLM baselines and standard fine-tuning methods on these complex benchmarks. Code is released at \url{https: //github. com/microsoft/HeurAgenix/tree/NaDRO}.

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AAAI Conference 2020 Conference Paper

RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

  • Ling Zhang
  • Chengjiang Long
  • Xiaolong Zhang
  • Chunxia Xiao

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated negative residual images and inverse illumination maps can be used to generate indirect shadow-removal images to refine the coarse shadow-removal result to the fine shadow-free image in a coarse-to-fine fashion. Three discriminators are designed to distinguish whether the predicted negative residual images, shadow-removal images, and the inverse illumination maps are real or fake jointly compared with the corresponding ground-truth information. To our best knowledge, we are the first one to explore residual and illumination for shadow removal. We evaluate our proposed method on two benchmark datasets, i. e. , SRD and ISTD, and the extensive experiments demonstrate that our proposed method achieves the superior performance to state-of-the-arts, although we have no particular shadow-aware components designed in our generators.

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JBHI Journal 2017 Journal Article

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

  • Ling Zhang
  • Le Lu
  • Isabella Nogues
  • Ronald M. Summers
  • Shaoxiong Liu
  • Jianhua Yao

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation- based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98. 3%), area under the curve (0. 99) values, and especially specificity (98. 3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

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IJCAI Conference 1985 Conference Paper

A Weighted Technique in Heuristic Search

  • Bo Zhang
  • Ling Zhang

As shown in [1], we examine search as a s t a t i s t i c s a m p l i n g p r o c e s s. Based o n some s t a t i s t i c a l i n f e r e n c e method the p r o b a b i l i t y t h a t a s u b t r e e i n search t r e e c o n t a i n s the g o a l can b e d e c i d e d. Thus some w e i g h t is in t e n t i o n a l y added to t h e e v a l u a t i o n f u n c t i o n o f those nodes which are u n l i k e l y i n the s o l u t i o n p a t h s o t h a t the s e a r c h w i l l c o n c e n t r a t e o n the most p r o m i s i n g p a t h. I t r e s u l t s in a new w e i g h t e d a l g o r i t h m - W S A. Tn a u n i f o r m m-ary t r e e, we show t h a t a g o a l can be found by WSA in the p o l y n o m i a l t i m e, a l t h o u g h t h e c o m p u t a t i o n a l c o m p l e x i t y of A ( o r A*) may be 0 ( e ) f o r s e a r c h i n g the same s p a c e. Where N i s the depth a t which t h e g o a l i s l o c a t e d.

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