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Zhiguo Wang

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9

NeurIPS Conference 2025 Conference Paper

A Gradient Guided Diffusion Framework for Chance Constrained Programming

  • Boyang Zhang
  • Zhiguo Wang
  • Ya-Feng Liu

Chance constrained programming (CCP) is a powerful framework for addressing optimization problems under uncertainty. In this paper, we introduce a novel Gradient-Guided Diffusion-based Optimization framework, termed GGDOpt, which tackles CCP through three key innovations. First, GGDOpt accommodates a broad class of CCP problems without requiring the knowledge of the exact distribution of uncertainty—relying solely on a set of samples. Second, to address the nonconvexity of the chance constraints, it reformulates the CCP as a sampling problem over the product of two distributions: an unknown data distribution supported on a nonconvex set and a Boltzmann distribution defined by the objective function, which fully leverages both first- and second-order gradient information. Third, GGDOpt has theoretical convergence guarantees and provides practical error bounds under mild assumptions. By progressively injecting noise during the forward diffusion process to convexify the nonconvex feasible region, GGDOpt enables guided reverse sampling to generate asymptotically optimal solutions. Experimental results on synthetic datasets and a waveform design task in wireless communications demonstrate that GGDOpt outperforms existing methods in both solution quality and stability with nearly 80\% overhead reduction.

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YNIMG Journal 2025 Journal Article

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images

  • Zhaomin Yao
  • Weiming Xie
  • Jiaming Chen
  • Ying Zhan
  • Xiaodan Wu
  • Yingxin Dai
  • Yusong Pei
  • Zhiguo Wang

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.

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NeurIPS Conference 2024 Conference Paper

RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

  • Dongyu Ru
  • Lin Qiu
  • Xiangkun Hu
  • Tianhang Zhang
  • Peng Shi
  • Shuaichen Chang
  • Cheng Jiayang
  • Cunxiang Wang

Despite Retrieval-Augmented Generation (RAG) has shown promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliability of measurements. In this paper, we propose a fine-grained evaluation framework, RAGChecker, that incorporates a suite of diagnostic metrics for both the retrieval and generation modules. Meta evaluation verifies that RAGChecker has significantly better correlations with human judgments than other evaluation metrics. Using RAGChecker, we evaluate 8 RAG systems and conduct an in-depth analysis of their performance, revealing insightful patterns and trade-offs in the design choices of RAG architectures. The metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems.

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AAAI Conference 2023 Conference Paper

Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework

  • Shuai Wang
  • Yanqing Xu
  • Zhiguo Wang
  • Tsung-Hui Chang
  • Tony Q. S. Quek
  • Defeng Sun

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.

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AAAI Conference 2022 Conference Paper

Generation-Focused Table-Based Intermediate Pre-training for Free-Form Question Answering

  • Peng Shi
  • Patrick Ng
  • Feng Nan
  • Henghui Zhu
  • Jun Wang
  • Jiarong Jiang
  • Alexander Hanbo Li
  • Rishav Chakravarti

Question answering over semi-structured tables has attracted significant attention in the NLP community. However, most of the existing work focus on questions that can be answered with short-form answer, i. e. the answer is often a table cell or aggregation of multiple cells. This can mismatch with the intents of users who want to ask more complex questions that require free-form answers such as explanations. To bridge the gap, most recently, pre-trained sequence-tosequence language models such as T5 are used for generating free-form answers based on the question and table inputs. However, these pre-trained language models have weaker encoding abilities over table cells and schema. To mitigate this issue, in this work, we present an intermediate pre-training framework, Generation-focused Table-based Intermediate Pre-training (GENTAP), that jointly learns representations of natural language questions and tables. GEN- TAP learns to generate via two training objectives to enhance the question understanding and table representation abilities for complex questions. Based on experimental results, models that leverage GENTAP framework outperform the existing baselines on FETAQA benchmark. The pre-trained models are not only useful for free-form question answering, but also for few-shot data-to-text generation task, thus showing good transfer ability by obtaining new state-of-the-art results.

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AAAI Conference 2021 Conference Paper

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

  • Peng Shi
  • Patrick Ng
  • Zhiguo Wang
  • Henghui Zhu
  • Alexander Hanbo Li
  • Jun Wang
  • Cicero Nogueira dos Santos
  • Bing Xiang

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM). However, based on a pilot study, we observe three issues of existing general-purpose language models when they are applied to text-to-SQL semantic parsers: fail to detect column mentions in the utterances, fail to infer column mentions from cell values, and fail to compose complex SQL queries. To mitigate these issues, we present a model pre-training framework, Generation- Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. GAP MODEL 1 is trained on 2M utterance-schema pairs and 30K utterance-schema-SQL triples, whose utterances are produced by generative models. Based on experimental results, neural semantic parsers that leverage GAP MODEL as a representation encoder obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-SQL benchmarks.

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AAAI Conference 2020 Conference Paper

Who Did They Respond to? Conversation Structure Modeling Using Masked Hierarchical Transformer

  • Henghui Zhu
  • Feng Nan
  • Zhiguo Wang
  • Ramesh Nallapati
  • Bing Xiang

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. Previous work usually took a pair of utterances to decide whether one utterance is the parent of the other. We believe the entire ancestral history is a very important information source to make accurate prediction. Therefore, we design a novel masking mechanism to guide the ancestor flow, and leverage the transformer model to aggregate all ancestors to predict parent utterances. Our experiments are performed on the Reddit dataset (Zhang, Culbertson, and Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In addition, we also report experiments on a new larger corpus from the Reddit platform and release this dataset. We show that the proposed model, that takes into account the ancestral history of the conversation, significantly outperforms several strong baselines including the BERT model on all datasets.

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AAAI Conference 2018 Conference Paper

R 3: Reinforced Ranker-Reader for Open-Domain Question Answering

  • Shuohang Wang
  • Mo Yu
  • Xiaoxiao Guo
  • Zhiguo Wang
  • Tim Klinger
  • Wei Zhang
  • Shiyu Chang
  • Gerry Tesauro

In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings1 such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a preselected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e. g. , wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant passages by an information retrieval component, combined with a reading comprehension model that “reads” the passages to generate an answer to the question. Performance in this setting lags well behind closed-domain performance. In this paper, we present a novel open-domain QA system called Reinforced Ranker-Reader (R3 ), based on two algorithmic innovations. First, we propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Second, we propose a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning. We report extensive experimental results showing that our method significantly improves on the state of the art for multiple open-domain QA datasets. 2

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IJCAI Conference 2017 Conference Paper

Bilateral Multi-Perspective Matching for Natural Language Sentences

  • Zhiguo Wang
  • Wael Hamza
  • Radu Florian

Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model. Given two sentences P and Q, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions P against Q and P against Q. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, a decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.

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