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Chenglin Miao

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11 papers

2 author rows

IROS Conference 2025 Conference Paper

Dual-Bubble Coordinated Acoustic Micromanipulator for Multidirectional Object Rotation *

Yuyang Li 0003
Zhongqiang Zhang
Chenglin Miao
Xu Du
Qiang Huang 0002
Tatsuo Arai
Xiaoming Liu 0007

Micromanipulation techniques struggle to achieve three-dimensional rotational control at the microscale without compromising biocompatibility or spatial flexibility. Conventional methods based on mechanical contact, optical forces, or confined microfluidics constrain dynamic reconfiguration and surgical accessibility. Here, we introduce a dual-bubble acoustic micromanipulator that enables multidirectional rotation through controlled hydrodynamic fields. By placing oscillating microbubbles at the tips of micropipettes, this system creates adjustable vortex patterns: a single microbubble generates toroidal flows for out-of-plane rotation, while two microbubbles produce shear forces for in-plane spinning. This approach uses simple mechanical adjustments to control rotational axes in open fluid environments, without needing frequency modulation or phase synchronization. Flow-field simulations and experiments with polystyrene microspheres confirm deterministic orientation control, and tests with shrimp embryos demonstrate rotation at clinically relevant speeds. The open architecture integrates seamlessly with standard microscopy and robotic injection systems, offering a non-contact, precise tool for applications such as polar body alignment, intracellular surgery, and 3-D imaging.

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AAAI Conference 2025 Short Paper

Neuron Explanations for Conformal Prediction (Student Abstract)

Divya Lidder
Kathryn Morse
Bridget Sullivan
Wei Qian
Chenglin Miao
Mengdi Huai

Conformal prediction (CP) has gained prominence as a popular technique for uncertainty quantification in deep neural networks (DNNs), providing statistically rigorous uncertainty sets. However, existing CP methods fail to clarify the origins of predictive uncertainties. While neuron-level interpretability has been effective in revealing the internal mechanisms of DNNs, explaining CP at the neuron level remains unexplored. Nonetheless, generating neuron explanations for CP is challenging due to the discrete and non-differentiable characteristics of CP, and the labor-intensive process of semantic annotation. To address these limitations, this paper proposes a novel neuron explanation approach for CP by identifying neurons crucial for understanding predictive uncertainties and automatically generating semantic explanations. The effectiveness of the proposed method is validated through both qualitative and quantitative experiments.

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AAAI Conference 2024 Conference Paper

Backdoor Attacks via Machine Unlearning

Zihao Liu
Tianhao Wang
Mengdi Huai
Chenglin Miao

As a new paradigm to erase data from a model and protect user privacy, machine unlearning has drawn significant attention. However, existing studies on machine unlearning mainly focus on its effectiveness and efficiency, neglecting the security challenges introduced by this technique. In this paper, we aim to bridge this gap and study the possibility of conducting malicious attacks leveraging machine unlearning. Specifically, we consider the backdoor attack via machine unlearning, where an attacker seeks to inject a backdoor in the unlearned model by submitting malicious unlearning requests, so that the prediction made by the unlearned model can be changed when a particular trigger presents. In our study, we propose two attack approaches. The first attack approach does not require the attacker to poison any training data of the model. The attacker can achieve the attack goal only by requesting to unlearn a small subset of his contributed training data. The second approach allows the attacker to poison a few training instances with a pre-defined trigger upfront, and then activate the attack via submitting a malicious unlearning request. Both attack approaches are proposed with the goal of maximizing the attack utility while ensuring attack stealthiness. The effectiveness of the proposed attacks is demonstrated with different machine unlearning algorithms as well as different models on different datasets.

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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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AAAI Conference 2022 Conference Paper

Towards Automating Model Explanations with Certified Robustness Guarantees

Mengdi Huai
Jinduo Liu
Chenglin Miao
Liuyi Yao
Aidong Zhang

Providing model explanations has gained significant popularity recently. In contrast with the traditional feature-level model explanations, concept-based explanations can provide explanations in the form of high-level human concepts. However, existing concept-based explanation methods implicitly follow a two-step procedure that involves human intervention. Specifically, they first need the human to be involved to define (or extract) the high-level concepts, and then manually compute the importance scores of these identified concepts in a post-hoc way. This laborious process requires significant human effort and resource expenditure due to manual work, which hinders their large-scale deployability. In practice, it is challenging to automatically generate the concept-based explanations without human intervention due to the subjectivity of defining the units of concept-based interpretability. In addition, due to its data-driven nature, the interpretability itself is also potentially susceptible to malicious manipulations. Hence, our goal in this paper is to free human from this tedious process, while ensuring that the generated explanations are provably robust to adversarial perturbations. We propose a novel concept-based interpretation method, which can not only automatically provide the prototype-based concept explanations but also provide certified robustness guarantees for the generated prototype-based explanations. We also conduct extensive experiments on real-world datasets to verify the desirable properties of the proposed method.

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NeurIPS Conference 2021 Conference Paper

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Xinyi Xu
Lingjuan Lyu
Xingjun Ma
Chenglin Miao
Chuan Sheng Foo
Bryan Kian Hsiang Low

In collaborative machine learning(CML), multiple agents pool their resources(e. g. , data) together for a common learning task. In realistic CML settings where the agents are self-interested and not altruistic, they may be unwilling to share data or model information without adequate rewards. Furthermore, as the data/model information shared by the agents may differ in quality, designing rewards which are fair to them is important so that they would not feel exploited nor discouraged from sharing. In this paper, we adopt federated learning as the CML paradigm, propose a novel cosine gradient Shapley value(CGSV) to fairly evaluate the expected marginal contribution of each agent’s uploaded model parameter update/gradient without needing an auxiliary validation dataset, and based on the CGSV, design a novel training-time gradient reward mechanism with a fairness guarantee by sparsifying the aggregated parameter update/gradient downloaded from the server as reward to each agent such that its resulting quality is commensurate to that of the agent’s uploaded parameter update/gradient. We empirically demonstrate the effectiveness of our fair gradient reward mechanism on multiple benchmark datasets in terms of fairness, predictive performance, and time overhead.

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AAAI Conference 2020 Conference Paper

Pairwise Learning with Differential Privacy Guarantees

Mengdi Huai
Di Wang
Chenglin Miao
Jinhui Xu
Aidong Zhang

Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i. e. , the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and ofﬂine settings. Speciﬁcally, for the online setting, we ﬁrst introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the ofﬂine setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis.

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AAAI Conference 2020 Conference Paper

Towards Interpretation of Pairwise Learning

Mengdi Huai
Di Wang
Chenglin Miao
Aidong Zhang

Recently, there are increasingly more attentions paid to an important family of learning problems called pairwise learning, in which the associated loss functions depend on pairs of instances. Despite the tremendous success of pairwise learning in many real-world applications, the lack of transparency behind the learned pairwise models makes it difﬁcult for users to understand how particular decisions are made by these models, which further impedes users from trusting the predicted results. To tackle this problem, in this paper, we study feature importance scoring as a speciﬁc approach to the problem of interpreting the predictions of black-box pairwise models. Speciﬁcally, we ﬁrst propose a novel adaptive Shapleyvalue-based interpretation method, based on which a vector of importance scores associated with the underlying features of a testing instance pair can be adaptively calculated with the consideration of feature correlations, and these scores can be used to indicate which features make key contributions to the ﬁnal prediction. Considering that Shapley-value-based methods are usually computationally challenging, we further propose a novel robust approximation interpretation method for pairwise models. This method is not only much more efﬁcient but also robust to data noise. To the best of our knowledge, we are the ﬁrst to investigate how to enable interpretation in pairwise learning. Theoretical analysis and extensive experiments demonstrate the effectiveness of the proposed methods.

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IJCAI Conference 2019 Conference Paper

Data Poisoning Attack against Knowledge Graph Embedding

Hengtong Zhang
Tianhang Zheng
Jing Gao
Chenglin Miao
Lu Su
Yaliang Li
Kui Ren

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

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IJCAI Conference 2019 Conference Paper

Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm

Mengdi Huai
Hongfei Xue
Chenglin Miao
Liuyi Yao
Lu Su
Changyou Chen
Aidong Zhang

As an effective way to learn a distance metric between pairs of samples, deep metric learning (DML) has drawn significant attention in recent years. The key idea of DML is to learn a set of hierarchical nonlinear mappings using deep neural networks, and then project the data samples into a new feature space for comparing or matching. Although DML has achieved practical success in many applications, there is no existing work that theoretically analyzes the generalization error bound for DML, which can measure how good a learned DML model is able to perform on unseen data. In this paper, we try to fill up this research gap and derive the generalization error bound for DML. Additionally, based on the derived generalization bound, we propose a novel DML method (called ADroDML), which can adaptively learn the retention rates for the DML models with dropout in a theoretically justified way. Compared with existing DML works that require predefined retention rates, ADroDML can learn the retention rates in an optimal way and achieve better performance. We also conduct experiments on real-world datasets to verify the findings derived from the generalization error bound and demonstrate the effectiveness of the proposed adaptive DML method.

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IJCAI Conference 2019 Conference Paper

Privacy-aware Synthesizing for Crowdsourced Data

Mengdi Huai
Di Wang
Chenglin Miao
Jinhui Xu
Aidong Zhang

Although releasing crowdsourced data brings many benefits to the data analyzers to conduct statistical analysis, it may violate crowd users' data privacy. A potential way to address this problem is to employ traditional differential privacy (DP) mechanisms and perturb the data with some noise before releasing them. However, considering that there usually exist conflicts among the crowdsourced data and these data are usually large in volume, directly using these mechanisms can not guarantee good utility in the setting of releasing crowdsourced data. To address this challenge, in this paper, we propose a novel privacy-aware synthesizing method (i. e. , PrisCrowd) for crowdsourced data, based on which the data collector can release users' data with strong privacy protection for their private information, while at the same time, the data analyzer can achieve good utility from the released data. Both theoretical analysis and extensive experiments on real-world datasets demonstrate the desired performance of the proposed method.

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