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Yue Yang

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25 papers

2 author rows

AAAI Conference 2026 Conference Paper

Anchor-Driven Nyström for Deep Graph-Level Clustering

Jiaxin Wang
Wenxuan Tu
Lingren Wang
Jieren Cheng
Yue Yang

Graph-level clustering (GLC), which aims to group entire graphs according to their structural and attribute-based similarities, represents a fundamental yet challenging task in various practical applications. Existing GLC methods primarily fall into two main paradigms: 1) deep graph clustering approaches based on Graph Neural Networks (GNNs), and 2) kernel-based methods that utilize predefined kernels to perform fine-grained structural comparison for clustering. However, GNN-based methods typically learn graph-level representations by aggregating node embeddings through pooling operations, which inevitably leads to substantial information loss and suboptimal clustering performance. In contrast, kernel methods, despite their theoretical expressiveness, suffer from prohibitive computational costs that hinder their scalability to large-scale settings. To solve these issues, we propose a novel graph learning framework named Anchor-driven Nyström for Deep Graph-Level Clustering (ANGC), which computes graph similarity via kernel methods while retaining the scalability of GNNs. Specifically, we first employ GNNs to encode individual graphs into sets of node embeddings. Rather than relying on pooling operations, we compute graph similarities in a kernel space constructed from these embeddings. To enhance both scalability and representational power, we introduce learnable graph Nyström anchors, which support end-to-end optimization and significantly accelerate kernel computations. To further improve the discriminative capability of these anchors, we propose the concept of anchor response discrepancy, that is, the variation in a given anchor’s responses across different samples. By maximizing this discrepancy, the anchors are encouraged to strengthen inter-graph distinctions for better clustering. Extensive experiments demonstrate the effectiveness and superiority of ANGC over existing state-of-the-art methods.

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JBHI Journal 2026 Journal Article

LLM-DDI: Leveraging Large Language Models for Drug-Drug Interaction Prediction on Biomedical Knowledge Graph

Dongxu Li
Yue Yang
Ziwen Cui
Hengchuang Yin
Pengwei Hu
Lun Hu

Drug-drug interaction (DDI) refers to the interaction relationships between drugs. Discovering new DDIs is crucial for advancing drug development and enhancing clinical treatments. Given the significant progress achieved through graph neural networks (GNNs), network-based models have become a prevalent approach for tackling this challenge. However, current network-based approaches are incapable of seamlessly integrating a wide range of information. Motivated by this discovery, we propose a novel model, namely LLM-DDI, which aims to comprehensively tackle DDI prediction tasks by integrating various information of molecules in the BKG. LLM-DDI initially incorporates the generative pre-trained transformer (GPT) model to generate embeddings for each molecule within the biomedical knowledge graph (BKG). These embeddings encompass diverse types of information pertaining to each molecule. Subsequently, LLM-DDI utilizes a message-passing GNN framework to enhance the learning of molecular representations with the embeddings derived from GPT as input. LLM-DDI governs the propagation of information within the BKG by semantic relationships. These semantic relationships determine how information flows and is exchanged between different entities in the BKG. Finally, LLM-DDI leverages the learned drug representations to predict potential DDIs. Experiments show the effectiveness of LLM-DDI, as it achieves the best performance on two real-world datasets, providing valuable guidance for drug development and clinical treatment.

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JBHI Journal 2026 Journal Article

Multi-View Contrastive Learning for Drug-Drug Interaction Event Prediction

Dongxu Li
Feifan Zhao
Yue Yang
Ziwen Cui
Pengwei Hu
Lun Hu

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse effects and compromised therapeutic efficacy. Accurate prediction of DDI events, which involve not only identifying interacting drug pairs but also characterizing the specific nature and context of their interactions, is essential for drug safety and personalized medicine. In this study, we propose a novel Multi-view Contrastive Learning framework, namely MCL-DDI, for DDI Event Prediction by leveraging multi-view representations of drugs to enhance predictive performance. MCL-DDI integrates molecular structures and network features, capturing complementary information about drug properties and interactions. By employing contrastive learning, we align and unify drug representations across these diverse views, enabling the framework to distinguish complex interaction patterns. Extensive experiments on benchmark datasets demonstrate that MCL-DDI outperforms state-of-the-art methods in terms of predictive accuracy. Furthermore, case studies highlight the model’s ability to identify clinically relevant DDIs, offering practical insights for drug development and risk assessment. Our work establishes a robust and accurate paradigm for DDI event prediction, paving the way for safer and more effective pharmacological interventions.

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AAAI Conference 2026 Conference Paper

In the federated clustering task, structural heterogeneity across clients inevitably impedes effective multi-source information sharing. To solve this issue, Personalized Federated Learning (PFL) has emerged as a potentially effective solution for image and text clustering. Unlike Euclidean data, graph-structured data exhibits diverse and fragile local patterns, which widely exist in real-world scenarios. Multi-graph data analysis in the federated learning setting is challenging and important, yet remains underexplored. This motivates us to propose a novel PERsonalized Federated graph-lEvel Clustering neTwork (PERFECT), which generates a specialized aggregation strategy for each client by uploading key model parameters and representative samples without sharing private information. Specifically, for each client, we first reconstruct privacy-preserving representative samples in a min-max optimization manner and then upload these samples to the server for subsequent personalized parameter aggregation. On the server, we first extract graph-level embeddings from the uploaded data, and then estimate affinities among multiple learned embeddings to formulate a personalized aggregation strategy for each client. Subsequently, to help each local model better identify the cluster boundaries, we utilize clustering-wise gradient to update the key components in the personalized model parameters from the server. Extensive experimental results have demonstrated the effectiveness and superiority of PERFECT over its competitors.

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AAAI Conference 2026 Conference Paper

SAR-DisentDM: A Semantic-Disentangled Diffusion Model for Limited-Data SAR Image Synthesis

Yue Yang
Song Tang
Qijun Zhao
Hailun Zhang
Xiwen Wang
Zijian Deng

The high cost of synthetic aperture radar (SAR) data acquisition motivates SAR image generation research. However, the data scarcity and SAR's inherent azimuth sensitivity make generative models suffer from severe azimuth overfitting. Most existing methods require supplementary data to work effectively, limiting their practicality. In this paper, we propose SAR-DisentDM, a novel semantic-disentangled diffusion model for limited-data SAR image generation, without requiring any auxiliary resources. We develop a physics-aware diffusion architecture that explicitly models semantic knowledge of SAR images, including intrinsic characteristics, contextual diversity, and measurement randomness. A key innovation is the attention-guided semantic disentanglement (AGSD) module, designed to decouple category-specific features from azimuth-variable scattering patterns. This is achieved by aid of a dual disentangled loss with time-step-adaptive optimization. Furthermore, we introduce an azimuth angle perturbation augmentation (AAPA) mechanism, to enhance the model's robustness to minor azimuth angle errors. Extensive evaluations validate that SAR-DisentDM enables controllable SAR image synthesis with designated attributes, significantly improving representation and generalization abilities under limited data. Synthetic imagery from our approach boosts automatic target recognition (ATR) accuracy beyond state-of-the-art methods.

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AAMAS Conference 2026 Conference Paper

Selective Amnesia: Observation Unlearning in Reinforcement Learning

Yue Yang
Jinhao Li
Hao Wang

Although the concept of machine unlearning has been widely explored in the past few years, unlearning in reinforcement learning (RL) models remains underdeveloped. In this paper, we undertake an in-depth exploration of reinforcement unlearning (RUL), a novel and challenging concept within the field of RL and machine unlearning. We investigate the inherent difficulties associated with RUL, pinpointing two critical factors that contribute to its complexity: agent-environment interactions and the sequential nature of decision-making. To tackle these challenges, we propose an unlearning algorithm that addresses the fundamentals of RUL from the perspective of environment observations, enabling observationlevel unlearning for both tabular and deep Q-learning. By quantitatively assessing the effects of observations through state-action values and modifying and retracing the policy trajectories establishedbytheoriginalmodel, wedemonstratethat, underreasonable assumptions, RUL can effectively eliminate both the immediate and subsequent impacts of the targeted unlearning observation. Empirical evaluations also validate the effectiveness of our RUL approach.

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ICLR Conference 2025 Conference Paper

Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model

Long Le
Jason Xie
William Liang
Hung-Ju Wang
Yue Yang
Yecheng Jason Ma 0001
Kyle Vedder
Arjun Krishna

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting their broader applications. To overcome this challenge, we present Articulate-Anything, a system that automates the articulation of diverse, complex objects from many input modalities, including text, images, and videos. Articulate-Anything leverages vision-language models (VLMs) to generate code that can be compiled into an interactable digital twin for use in standard 3D simulators. Our system exploits existing 3D asset datasets via a mesh retrieval mechanism, along with an actor-critic system that iteratively proposes, evaluates, and refines solutions for articulating the objects, self-correcting errors to achieve a robust out- come. Qualitative evaluations demonstrate Articulate-Anything's capability to articulate complex and even ambiguous object affordances by leveraging rich grounded inputs. In extensive quantitative experiments on the standard PartNet-Mobility dataset, Articulate-Anything substantially outperforms prior work, increasing the success rate from 8.7-11.6\% to 75\% and setting a new bar for state-of-art performance. We further showcase the utility of our generated assets by using them to train robotic policies for fine-grained manipulation tasks that go beyond basic pick and place.

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ICML Conference 2025 Conference Paper

Competitively Consistent Clustering

Niv Buchbinder
Roie Levin
Yue Yang

In fully-dynamic consistent clustering, we are given a finite metric space $(M, d)$, and a set $F\subseteq M$ of possible locations for opening centers. Data points arrive and depart, and the goal is to maintain an approximately optimal clustering solution at all times while minimizing the recourse, the total number of additions/deletions of centers over time. Specifically, we study fully dynamic versions of the classical $k$-center, facility location, and $k$-median problems. We design algorithms that, given a parameter $\beta\geq 1$, maintain an $O(\beta)$-approximate solution at all times, and whose total recourse is bounded by $O(\log |F| \log \Delta) \cdot OPT_{rec}^{\beta}$. Here $OPT_{rec}^{\beta}$ is the minimal recourse of an offline algorithm that maintains a $\beta$-approximate solution at all times, and $\Delta$ is the metric aspect ratio. We obtain our results via a reduction to the recently proposed Positive Body Chasing framework of [Bhattacharya Buchbinder Levin Saranurak, FOCS 2023], which we show gives fractional solutions to our clustering problems online. Our contribution is to round these fractional solutions while preserving the approximation and recourse guarantees. We complement our positive results with logarithmic lower bounds which show that our bounds are nearly tight.

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ICLR Conference 2025 Conference Paper

Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping

Yue Yang
Shuibo Zhang
Kaipeng Zhang
Yi Bin
Yu Wang 0002
Ping Luo 0002
Wenqi Shao

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks such as visual perception and reasoning, leading to good performance on various multimodal evaluation benchmarks. However, these benchmarks keep a static nature and overlap with the pre-training data, resulting in fixed complexity constraints and data contamination issues. This raises the concern regarding the validity of the evaluation. To address these two challenges, we introduce a dynamic multimodal evaluation protocol called Vision-Language Bootstrapping (VLB). VLB provides a robust and comprehensive assessment for LVLMs with reduced data contamination and flexible complexity. To this end, VLB dynamically generates new visual question-answering samples through a multimodal bootstrapping module that modifies both images and language, while ensuring that newly generated samples remain consistent with the original ones by a judge module. By composing various bootstrapping strategies, VLB offers dynamic variants of existing benchmarks with diverse complexities, enabling the evaluation to co-evolve with the ever-evolving capabilities of LVLMs. Extensive experimental results across multiple benchmarks, including SEEDBench, MMBench, and MME, show that VLB significantly reduces data contamination and exposes performance limitations of LVLMs.

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NeurIPS Conference 2025 Conference Paper

Feature Unlearning: Theoretical Foundations and Practical Applications with Shuffling

Yue Yang
Jinhao Li
Hao Wang

Machine unlearning has become a focal point in recent research, yet the specific area of feature unlearning has not been thoroughly explored. Feature unlearning involves the elimination of specific features' effects from an already trained model, presenting distinct challenges that are still not comprehensively addressed. This paper presents a novel and straightforward approach to feature unlearning that employs a tactical shuffling of the features designated for removal. By redistributing the values of the features targeted for unlearning throughout the original training dataset and subsequently fine-tuning the model with this shuffled data, our proposed method provides a theoretical guarantee for effective feature unlearning. Under mild assumptions, our method can effectively disrupt the established correlations between unlearned features and the target outcomes, while preserving the relationships between the remaining features and the predicted outcomes. Our empirical studies across various datasets, validate that our approach not only successfully removes the effects of specified features but also maintains the informational integrity of the remaining features while achieving a faster convergence rate.

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JBHI Journal 2025 Journal Article

Medical Hyperspectral Image Feature Selection Framework Using Functional Data Analysis: Application to Membranous Nephropathy Pathologic Diagnosis

Meng Lv
Shiyu Liu
Xiaoying Ma
Yue Yang
Haihao Zhang
Wei Li

To address the core issue of high-dimensional data processing in hyperspectral pathological diagnosis, we develop a new feature selection framework using functional data analysis (FSFDA). The framework models pixel spectra as continuous functions to preserve spectral continuity, overcoming the limitations of traditional discrete representations. Based on functional features, an innovative adaptive spectral segmentation strategy driven by functional change rate is developed to achieve optimal segmentation in the feature space. Additionally, a multi-criteria scoring mechanism including supervised (FSFDA-S) and unsupervised (FSFDA-U) paradigms is developed to enhance feature diagnostic discriminability while maintaining sparsity. Experimental results on the pathological hyperspectral image dataset of membranous nephropathy validate that the proposed method achieves over 99% classification accuracy while reducing feature dimensions by 94. 5%. For cross-modal data involving in-vivo human brain and white blood cells, FSFDA effectively identifies diagnostic bands aligned with histopathological signatures, verifying its adaptive feature selection ability and cross sample generalization performance.

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AAMAS Conference 2025 Conference Paper

Self-Interpretable Reinforcement Learning via Rule Ensembles

Yue Yang
Fan Yang
Yu Bai
Hao Wang

Current reinforcement learning (RL) models, often functioning as complex ‘black boxes, ’ obscure decision-making processes. This lack of transparency limits its applicability in critical real-world applications where clear reasoning behind algorithmic choices is crucial. To tackle this issue, we suggest moving from neural network or tabular approaches to a rule ensemble model, which improves decision-making clarity and adapts dynamically to environmental interactions. Instead, our method constructs additive rule ensembles to approximate the Q-value in reinforcement learning using orthogonal gradient boosting (OGB) combined with a post-processing rule replacement technique. This method enables the model to provide inherent explanations through the use of rules. Our study sets a theoretical foundation for rule ensembles within the reinforcement learning framework, emphasizing their capacity to boost interpretability and facilitate the analysis of rule impacts. Experimental results from seven classic environments demonstrate that our proposed rule ensembles match or exceed the performance of representative RL models such as DQN, A2C, and PPO, while also providing self-interpretability and transparency.

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AAMAS Conference 2025 Conference Paper

Using Assistance Rewards Without Introducing Bias: Overcoming Sparse Rewards in Multi-Agent Reinforcement Learning

Yue Yang
Bernd Meyer
Frits de Nijs

Reinforcement learning agents may fail to learn good policies when their reward function is too sparse. Auxiliary reward shaping functions can help guide exploration towards the true rewards, but risk producing sub-optimal policies as agents now target a modified objective function. Our paper addresses this challenge by introducing a general framework for incorporating auxiliary reward functions without introducing a bias in the true objective. Agents train an ensemble of reward-function-specific policies, sharing experiences collected with one policy to all other policies in the ensemble. A top-level control policy then learns to choose the best policy to maximize the true objective. We show that this scheme does not affect the convergence properties of the underlying reinforcement learning algorithm, while avoiding potential biasing of the agent’s objective. We also adapted our proposed algorithm using off-policy PPO with MA-Trace correction for state value estimation. To our knowledge, this is the first work to adapt off-policy PPO in a multiagent setting. We also demonstrate that our approach operates effectively with various assistance reward designs, removing the need for detailed reward function crafting or fine-tuning.

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NeurIPS Conference 2024 Conference Paper

A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis

Yue Yang
Mona Gandhi
Yufei Wang
Yifan Wu
Michael S. Yao
Chris Callison-Burch
James C. Gee
Mark Yatskar

While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. A key finding we show empirically is that existing visual backbones lack an appropriate prior from the architecture for reliable generalization in these settings. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. To this end, we introduce Knowledge-enhanced Bottlenecks (KnoBo), a class of concept bottleneck models that incorporates knowledge priors that constrain it to reason with clinically relevant factors found in medical textbooks or PubMed. KnoBo uses retrieval-augmented language models to design an appropriate concept space paired with an automatic training procedure for recognizing the concept. We evaluate different resources of knowledge and recognition architectures on a broad range of domain shifts across 20 datasets. In our comprehensive evaluation with two imaging modalities, KnoBo outperforms fine-tuned models on confounded datasets by 32. 4% on average. Finally, evaluations reveal that PubMed is a promising resource for making medical models less sensitive to domain shift, outperforming other resources on both diversity of information and final prediction performance.

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NeurIPS Conference 2024 Conference Paper

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Ablation Capability for Large Vision-Language Models

Shuo Liu
Kaining Ying
Hao Zhang
Yue Yang
Yuqi Lin
Tianle Zhang
Chuanhao Li
Yu Qiao

Multi-turn visual conversation is an important ability of real-world AI assistants. However, the related evaluation benchmark is missed. This paper presents ConvBench, a multi-turn conversation benchmark with hierarchical capabilities ablation evaluation for Large Vision-Language Models (LVLMs). ConvBench comprises 577 curated multi-turn conversations, encompassing 215 tasks. These tasks are broad and open-ended, which resemble real-world user behaviors. ConvBench progressively examines the LVLMs' perception, reasoning, and creativity capabilities in each conversation and can decouple these capabilities in evaluations and thus perform reliable error attribution. Besides, considering the diversity of open-ended questions, we introduce an efficient and reliable automatic evaluation framework. Experimental results reveal that ConvBench is a significant challenge for current LVLMs, even for GPT4V, which achieves only a 39. 51% score. Besides, we have some insightful findings, such as the weak perception of LVLMs inhibits authentic strengths in reasoning and creation. We believe our design of hierarchical capabilities, decoupling capabilities evaluation, and multi-turn conversation can blaze a new trail in LVLMs evaluation. Code and benchmark are released at https: //github. com/shirlyliu64/ConvBench.

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AAAI Conference 2024 Conference Paper

Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification

Wenshuo Peng
Kaipeng Zhang
Yue Yang
Hao Zhang
Yu Qiao

Vision-language foundation models have been incredibly successful in a wide range of downstream computer vision tasks using adaptation methods. However, due to the high cost of obtaining pre-training datasets, pairs with weak image-text correlation in the data exist in large numbers. We call them weak-paired samples. Due to the limitations of these weak-paired samples, the pre-training model are unable to mine all the knowledge from pre-training data. The existing adaptation methods do not consider the missing knowledge, which may lead to crucial task-related knowledge for the downstream tasks being ignored. To address this issue, we propose a new adaptation framework called Data Adaptive Traceback (DAT). Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data to enable the downstream tasks. Furthermore, we adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning. We conduct extensive experiments that show our proposed DAT approach meaningfully improves various benchmark datasets’ performance over traditional adaptation methods by simply.

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JBHI Journal 2024 Journal Article

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering

Guodong Li
Bowei Zhao
Xiaorui Su
Yue Yang
Pengwei Hu
Xi Zhou
Lun Hu

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

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ICML Conference 2024 Conference Paper

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Kaining Ying
Fanqing Meng
Jin Wang
Zhiqian Li
Han Lin
Yue Yang
Hao Zhang 0117
Wenbo Zhang 0009

Large Vision-Language Models (LVLMs) show significant strides in general-propose multimodal applications such as visual dialogue and embodied navigation. However, existing multimodal evaluation benchmarks cover a limited number of multimodal tasks testing rudimentary capabilities, falling short in tracking LVLM development. In this study, we present MMT-Bench, a comprehensive benchmark designed to assess LVLMs across massive multimodal tasks requiring expert knowledge and deliberate visual recognition, localization, and reasoning. MMT-Bench comprises $31, 325$ meticulously curated multi-choice visual questions from various multimodal scenarios such as vehicle driving and embodied navigation, covering $32$ core meta-tasks and $162$ subtasks in multimodal understanding. Due to its extensive task coverage, MMT-Bench enables the evaluation of LVLMs using a task map, facilitating the discovery of in- and out-of-domain tasks. Evaluation results involving $20$ publicly available LVLMs such as the proprietary GeminiProVision model, underscore the significant challenges posed by MMT-Bench. We anticipate that MMT-Bench will inspire the community to develop next-generation multimodal foundation models aimed at achieving general-purpose multimodal intelligence.

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JBHI Journal 2024 Journal Article

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network

Bo-Wei Zhao
Yi-Zhou He
Xiao-Rui Su
Yue Yang
Guo-Dong Li
Yu-An Huang
Peng-Wei Hu
Zhu-Hong You

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives.

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ICML Conference 2024 Conference Paper

Position: Towards Implicit Prompt For Text-To-Image Models

Yue Yang
Yuqi Lin
Hong Liu
Wenqi Shao
Runjian Chen
Hailong Shang
Yu Wang 0002
Yu Qiao 0001

Recent text-to-image (T2I) models have had great success, and many benchmarks have been proposed to evaluate their performance and safety. However, they only consider explicit prompts while neglecting implicit prompts (hint at a target without explicitly mentioning it). These prompts may get rid of safety constraints and pose potential threats to the applications of these models. This position paper highlights the current state of T2I models toward implicit prompts. We present a benchmark named ImplicitBench and conduct an investigation on the performance and impacts of implicit prompts with popular T2I models. Specifically, we design and collect more than 2, 000 implicit prompts of three aspects: General Symbols, Celebrity Privacy, and Not-Safe-For-Work (NSFW) Issues, and evaluate six well-known T2I models’ capabilities under these implicit prompts. Experiment results show that (1) T2I models are able to accurately create various target symbols indicated by implicit prompts; (2) Implicit prompts bring potential risks of privacy leakage for T2I models. (3) Constraints of NSFW in most of the evaluated T2I models can be bypassed with implicit prompts. We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.

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NeurIPS Conference 2024 Conference Paper

Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability, Reproducibility, and Practicality

Tianle Zhang
Langtian Ma
Yuchen Yan
Yuchen Zhang
Kai Wang
Yue Yang
Ziyao Guo
Wenqi Shao

Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite these strides, evaluating these models poses substantial challenges. Primarily, due to the limitations inherent in automatic metrics, manual evaluation is often considered a superior method for assessing T2V generation. However, existing manual evaluation protocols face reproducibility, reliability, and practicality issues. To address these challenges, this paper introduces the Text-to-Video Human Evaluation (T2VHE) protocol, a comprehensive and standardized protocol for T2V models. The T2VHE protocol includes well-defined metrics, thorough annotator training, and an effective dynamic evaluation module. Experimental results demonstrate that this protocol not only ensures high-quality annotations but can also reduce evaluation costs by nearly 50\%. We will open-source the entire setup of the T2VHE protocol, including the complete protocol workflow, the dynamic evaluation component details, and the annotation interface code. This will help communities establish more sophisticated human assessment protocols.

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JBHI Journal 2023 Journal Article

Microscopic Hyperspectral Image Classification Based on Fusion Transformer With Parallel CNN

Weijia Zeng
Wei Li
Mengmeng Zhang
Hao Wang
Meng Lv
Yue Yang
Ran Tao

Microscopic hyperspectral image (MHSI) has received considerable attention in the medical field. The wealthy spectral information provides potentially powerful identification ability when combining with advanced convolutional neural network (CNN). However, for high-dimensional MHSI, the local connection of CNN makes it difficult to extract the long-range dependencies of spectral bands. Transformer overcomes this problem well because of its self-attention mechanism. Nevertheless, transformer is inferior to CNN in extracting spatial detailed features. Therefore, a classification framework integrating transformer and CNN in parallel, named as Fusion Transformer (FUST), is proposed for MHSI classification tasks. Specifically, the transformer branch is employed to extract the overall semantics and capture the long-range dependencies of spectral bands to highlight the key spectral information. The parallel CNN branch is designed to extract significant multiscale spatial features. Furthermore, the feature fusion module is developed to effectively fuse and process the features extracted by the two branches. Experimental results on three MHSI datasets demonstrate that the proposed FUST achieves superior performance when compared with state-of-the-art methods.

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TIST Journal 2023 Journal Article

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

Wenyuan Yang
Shuo Shao
Yue Yang
Xiyao Liu
Ximeng Liu
Zhihua Xia
Gerald Schaefer
Hui Fang

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in a secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this article, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To the best of our knowledge, it is the first scheme to embed the watermark to models under a secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure that the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.

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AAMAS Conference 2021 Conference Paper

Deceptive Reinforcement Learning for Privacy-Preserving Planning

Zhengshang Liu
Yue Yang
Tim Miller
Peta Masters

In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour. A key ingredient in reinforcement learning is a reward function, which determines how much reward (negative or positive) is given and when. However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used. We define the problem of privacy-preserving reinforcement learning, and present two models for solving it. These models are based on dissimulation – a form of deception that ‘hides the truth’. We evaluate our models both computationally and via human behavioural experiments. Results show that the resulting policies are indeed deceptive, and that participants can determine the true reward function less reliably than that of an honest agent.

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JBHI Journal 2021 Journal Article

Spatial-Spectral Density Peaks-Based Discriminant Analysis for Membranous Nephropathy Classification Using Microscopic Hyperspectral Images

Meng Lv
Wei Li
Ran Tao
Nigel H. Lovell
Yue Yang
Tianqi Tu
Wenge Li

The traditional differential diagnosis of membranous nephropathy (MN) mainly relies on clinical symptoms, serological examination and optical renal biopsy. However, there is a probability of false positives in the optical inspection results, and it is unable to detect the change of biochemical components, which poses an obstacle to pathogenic mechanism analysis. Microscopic hyperspectral imaging can reveal detailed component information of immune complexes, but the high dimensionality of microscopic hyperspectral image brings difficulties and challenges to image processing and disease diagnosis. In this paper, a novel classification framework, including spatial-spectral density peaks-based discriminant analysis (SSDP), is proposed for intelligent diagnosis of MN using a microscopic hyperspectral pathological dataset. SSDP constructs a set of graphs describing intrinsic structure of MHSI in both spatial and spectral domains by employing density peak clustering. In the process of graph embedding, low-dimensional features with important diagnostic information in the immune complex are obtained by compacting the spatial-spectral local intra-class pixels while separating the spectral inter-class pixels. For the MN recognition task, a support vector machine (SVM) is used to classify pixels in the low-dimensional space. Experimental validation data employ two types of MN that are difficult to distinguish with optical microscope, including primary MN and hepatitis B virus-associated MN. Experimental results show that the proposed SSDP achieves a sensitivity of 99. 36%, which has potential clinical value for automatic diagnosis of MN.

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