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Muchao Ye

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6 papers

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TMLR Journal 2026 Journal Article

OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control

Darryl C. Jacob
Xinyu Liu
Muchao Ye
Xiaoyong Yuan
Pan He

Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional reinforcement learning–based TSC methods function as black boxes, providing little to no insight into their decisions. Although large language models (LLMs) could provide the needed interpretability through natural language reasoning, they face challenges such as limited memory and difficulty in deriving optimal policies from sparse environmental feedback. Existing TSC methods that apply reinforcement fine-tuning to LLMs face notable training instability and deliver only limited improvements over pretrained models. We attribute this instability to the long-horizon nature of TSC: feedback is sparse and delayed, most control actions yield only marginal changes in congestion metrics, and the resulting weak reward signals interact poorly with policy-gradient optimization. We introduce OracleTSC, which addresses these issues through: (1) a reward hurdle mechanism that filters weak learning signals by subtracting a calibrated threshold from environmental feedback, and (2) preventing policy degeneracy by maximizing the probability of the chosen answer, which promotes consistent decision-making across multiple responses. Experiments on the standard LibSignal benchmark demonstrate that our approach enables a compact model (LLaMA3-8B) to achieve substantial improvements in traffic flow, with a $75%$ reduction in travel time and $67%$ decrease in queue lengths over the pretrained baseline while preserving interpretability through natural language explanations. Furthermore, the method exhibits strong cross-intersection generalization: a policy trained on one intersection transfers to a structurally distinct intersection with $17%$ lower travel time and $39%$ lower queue length, all without any additional finetuning for the target topology. These findings show that uncertainty-aware reward shaping could stabilize reinforcement fine-tuning and provide a new perspective for improving its effectiveness in TSC tasks.

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IJCAI Conference 2024 Conference Paper

Recent Advances in Predictive Modeling with Electronic Health Records

Jiaqi Wang
Junyu Luo
Muchao Ye
Xiaochen Wang
Yuan Zhong
Aofei Chang
Guanjie Huang
Ziyi Yin

The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.

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AAAI Conference 2024 Conference Paper

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

Ziyi Yin
Muchao Ye
Tianrong Zhang
Jiaqi Wang
Han Liu
Jinghui Chen
Ting Wang
Fenglong Ma

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. Although the “pre-training & finetuning” learning paradigm significantly improves the VQA performance, the adversarial robustness of such a learning paradigm has not been explored. In this paper, we delve into a new problem: using a pre-trained multimodal source model to create adversarial image-text pairs and then transferring them to attack the target VQA models. Correspondingly, we propose a novel VQATTACK model, which can iteratively generate both im- age and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module. At each iteration, the LLM-enhanced image attack module first optimizes the latent representation-based loss to generate feature-level image perturbations. Then it incorporates an LLM to further enhance the image perturbations by optimizing the designed masked answer anti-recovery loss. The cross-modal joint attack module will be triggered at a specific iteration, which updates the image and text perturbations sequentially. Notably, the text perturbation updates are based on both the learned gradients in the word embedding space and word synonym-based substitution. Experimental results on two VQA datasets with five validated models demonstrate the effectiveness of the proposed VQATTACK in the transferable attack setting, compared with state-of-the-art baselines. This work reveals a significant blind spot in the “pre-training & fine-tuning” paradigm on VQA tasks. The source code can be found in the link https://github.com/ericyinyzy/VQAttack.

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NeurIPS Conference 2023 Conference Paper

UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation

Muchao Ye
Ziyi Yin
Tianrong Zhang
Tianyu Du
Jinghui Chen
Ting Wang
Fenglong Ma

Recent years have witnessed a surge of certified robust training pipelines against text adversarial perturbation constructed by synonym substitutions. Given a base model, existing pipelines provide prediction certificates either in the discrete word space or the continuous latent space. However, they are isolated from each other with a structural gap. We observe that existing training frameworks need unification to provide stronger certified robustness. Additionally, they mainly focus on building the certification process but neglect to improve the robustness of the base model. To mitigate the aforementioned limitations, we propose a unified framework named UniT that enables us to train flexibly in either fashion by working in the word embedding space. It can provide a stronger robustness guarantee obtained directly from the word embedding space without extra modules. In addition, we introduce the decoupled regularization (DR) loss to improve the robustness of the base model, which includes two separate robustness regularization terms for the feature extraction and classifier modules. Experimental results on widely used text classification datasets further demonstrate the effectiveness of the designed unified framework and the proposed DR loss for improving the certified robust accuracy.

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NeurIPS Conference 2023 Conference Paper

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

Ziyi Yin
Muchao Ye
Tianrong Zhang
Tianyu Du
Jinguo Zhu
Han Liu
Jinghui Chen
Ting Wang

Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks. Towards this end, we propose VLATTACK to generate adversarial samples by fusing perturbations of images and texts from both single-modal and multi-modal levels. At the single-modal level, we propose a new block-wise similarity attack (BSA) strategy to learn image perturbations for disrupting universal representations. Besides, we adopt an existing text attack strategy to generate text perturbations independent of the image-modal attack. At the multi-modal level, we design a novel iterative cross-search attack (ICSA) method to update adversarial image-text pairs periodically, starting with the outputs from the single-modal level. We conduct extensive experiments to attack three widely-used VL pretrained models for six tasks on eight datasets. Experimental results show that the proposed VLATTACK framework achieves the highest attack success rates on all tasks compared with state-of-the-art baselines, which reveals a significant blind spot in the deployment of pre-trained VL models.

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AAAI Conference 2022 Conference Paper

TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text

Muchao Ye
Chenglin Miao
Ting Wang
Fenglong Ma

This paper focuses on a newly challenging setting in hardlabel adversarial attacks on text data by taking the budget information into account. Although existing approaches can successfully generate adversarial examples in the hard-label setting, they follow an ideal assumption that the victim model does not restrict the number of queries. However, in realworld applications the query budget is usually tight or limited. Moreover, existing hard-label adversarial attack techniques use the genetic algorithm to optimize discrete text data by maintaining a number of adversarial candidates during optimization, which can lead to the problem of generating low-quality adversarial examples in the tight-budget setting. To solve this problem, in this paper, we propose a new method named TextHoaxer by formulating the budgeted hard-label adversarial attack task on text data as a gradientbased optimization problem of perturbation matrix in the continuous word embedding space. Compared with the genetic algorithm-based optimization, our solution only uses a single initialized adversarial example as the adversarial candidate for optimization, which significantly reduces the number of queries. The optimization is guided by a new objective function consisting of three terms, i. e. , semantic similarity term, pair-wise perturbation constraint, and sparsity constraint. Semantic similarity term and pair-wise perturbation constraint can ensure the high semantic similarity of adversarial examples from both comprehensive text-level and individual word-level, while the sparsity constraint explicitly restricts the number of perturbed words, which is also helpful for enhancing the quality of generated text. We conduct extensive experiments on eight text datasets against three representative natural language models, and experimental results show that TextHoaxer can generate high-quality adversarial examples with higher semantic similarity and lower perturbation rate under the tight-budget setting.

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