GenAI4ELab/papercli-papers huggingface dataset:
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning
Authors: Daoyuan Li; Zuyuan Yang; Shengli Xie
Venue: AAAI 2025 (main)

Abstract: Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms that address communication bottlenecks and enhance privacy protection, existing works overlook the impact of differences in data feature dimensions, resulting in global models that disproportionately depend on participants with large feature dimensions. Additionally, current single-view federated learning methods fail to account for the unique characteristics of multi-view data, leading to suboptimal performance in processing such data. To address these issues, we propose a Self-expressive Hypergraph Based Federated Multi-view Learning method (FedMSGL). The proposed method leverages self-expressive character in the local training to learn uniform dimension subspace with latent sample relation. At the central side, an adaptive fusion technique is employed to generate the global model, while constructing a hypergraph from the learned global and view-specific subspace to capture intricate interconnections across views. Experiments on multi-view datasets with different feature dimensions validated the effectiveness of the proposed method.
Marginal Benefit Driven RL Teacher for Unsupervised Environment Design
Authors: Dexun Li; Wenjun Li; Pradeep Varakantham
Venue: AAAI 2025 (main)

Abstract: Training generally capable agents in complex environments is a challenging task that involves identifying "right" environments at the training stage. Recent research has highlighted the potential of the Unsupervised Environment Design framework, which generates environment instances/levels adaptively at the frontier of the agent’s capabilities using regret measures. While regret approaches have shown great promise in generating feasible environments, they can produce difficult environments that are challenging for an RL agent to learn from. This is because regret represents the best-case (upper bound) learning potential and not the actual learning potential of an environment. To address this limitation, we propose an alternative mechanism that employs marginal benefit, focusing on the improvement (in terms of generalized performance) the agent policy gets for a given environment. The advantage of this new mechanism is that it is agent-focused (and not environment focused) and generates the "right" environments depending on the agent's policy. Additionally, to improve the generalizability of the agent, we introduce representative state diversity metric that aims to generate varied experiences for the agent. Finally, we provide detailed experimental results and ablation analysis to showcase the effectiveness of our new methods. We obtain SOTA results among RL based environment generation methods.
AIA: Autoregression-Based Injection Attacks Against Text2SQL Models
Authors: Deyin Li; Xiang Ling; Changjiang Li; Xiang Chen; Chunming Wu
Venue: AAAI 2025 (main)

Abstract: To facilitate understanding of users' diverse queries against the back-end databases in web applications, researchers have introduced Text-to-SQL (Text2SQL) models that can generate well-structured SQL queries from users' query texts in natural language. As the Text2SQL model decouples the user queries with the back-end databases, it inherently mitigates the SQL injection risk posed by inserting users' input into pre-written SQL queries. However, what security risks to web applications may be posed by Text2SQL models remains an open question. In this paper, we present a new attack framework, named Autoregression-based Injection Attacks (AIA), to evaluate the security risks of Text2SQL models. In particular, AIA makes target models generate attack payloads by constructing specific inputs and adjusting the input auto-regressively. Our evaluation demonstrates that AIA can cause Text2SQL models to generate target output by adversarial inputs with success rates of over 70% in most scenarios. The generated adversarial input has certain transferability in target Text2SQL models. Additionally, practice experiments show that AIA can make Text2SQL models extract user lists from databases and even delete data in databases directly.
k-HyperEdge Medoids for Clustering Ensemble
Authors: Feijiang Li; Jieting Wang; Liuya Zhang; Yuhua Qian; Shuai Jin; Tao Yan; Liang Du
Venue: AAAI 2025 (main)

Abstract: Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods have been proposed, most of which can be categorized into clustering-view and sample-view methods. The clustering-view method is generally efficient, but it could be affected by the unreliability that existed in base clustering results. The sample-view method shows good performance, while the construction of the pairwise sample relation is time-consuming. In this paper, the clustering ensemble is formulated as a k-HyperEdge Medoids discovery problem and a clustering ensemble method based on k-HyperEdge Medoids that considers the characteristics of the above two types of clustering ensemble methods is proposed. In the method, a set of hyperedges is selected from the clustering view efficiently, then the hyperedges are diffused and adjusted from the sample view guided by a hyperedge loss function to construct an effective k-HyperEdge Medoid set. The loss function is mainly reduced by assigning samples to the hyperedge with the highest degree of belonging. Theoretical analyses show that the solution can approximate the optimal, the assignment method can gradually reduce the loss function, and the estimation of the belonging degree is statistically reasonable. Experiments on artificial data show the working mechanism of the proposed method. The convergence of the method is verified by experimental analysis of twenty data sets. The effectiveness and efficiency of the proposed method are also verified on these data, with nine representative clustering ensemble algorithms as reference.
AdvDisplay: Adversarial Display Assembled by Thermoelectric Cooler for Fooling Thermal Infrared Detectors
Authors: Hao Li; Fanggao Wan; Yue Su; Yue Wu; Mingyang Zhang; Maoguo Gong
Venue: AAAI 2025 (main)

Abstract: When the current physical adversarial patches cannot deceive thermal infrared detectors, the existing techniques implement adversarial attacks from scratch, such as digital patch generation, material production, and physical deployment. Besides, it is difficult to finely regulate infrared radiation. To address these issues, this paper designs an adversarial thermal display (AdvDisplay ) by assembling thermoelectric coolers (TECs) as an array. Specifically, to reduce the gap between patches in the physical and digital worlds and decrease the power of AdvDisplay device, heat transfer loss and electric power loss are designed to guide the patch optimization. In addition, a precise temperature control scheme for AdvDisplay is proposed based on proportional-integral-derivative (PID) control. Due to the accurate temperature regulation and the reusability of AdvDisplay , our method is able to improve the attack success rate and the efficiency of physical deployments. Extensive experimental results indicate that the proposed method possesses superior adversarial effectiveness compared to other methods and demonstrates strong robustness in physical attacks.
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks
Authors: Haoyang Li; Li Bai; Qingqing Ye; Haibo Hu; Yaxin Xiao; Huadi Zheng; Jianliang Xu
Venue: AAAI 2025 (main)

Abstract: Model Inversion (MI) attacks, which reconstruct the training dataset of neural networks, pose significant privacy concerns in machine learning. Recent MI attacks have managed to reconstruct realistic label-level private data, such as the general appearance of a target person from all training images labeled on him. Beyond label-level privacy, in this paper we show sample-level privacy, the private information of a single target sample, is also important but under-explored in the MI literature due to the limitations of existing evaluation metrics. To address this gap, this study introduces a novel metric tailored for training-sample analysis, namely, the Diversity and Distance Composite Score (DDCS), which evaluates the reconstruction fidelity of each training sample by encompassing various MI attack attributes. This, in turn, enhances the precision of sample-level privacy assessments.

Leveraging DDCS as a new evaluative lens, we observe that many training samples remain resilient against even the most advanced MI attack. As such, we further propose a transfer learning framework that augments the generative capabilities of MI attackers through the integration of entropy loss and natural gradient descent. Extensive experiments verify the effectiveness of our framework on improving state-of-the-art MI attacks over various metrics including DDCS, coverage and FID. Finally, we demonstrate that DDCS can also be useful for MI defense, by identifying samples susceptible to MI attacks in an unsupervised manner.
Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach
Authors: Hebei Li; Yansong Peng; Jiahui Yuan; Peixi Wu; Jin Wang; Yueyi Zhang; Xiaoyan Sun
Venue: AAAI 2025 (main)

Abstract: Event cameras have recently been introduced into image semantic segmentation, owing to their high temporal resolution and other advantageous properties. However, existing event-based semantic segmentation methods often fail to fully exploit the complementary information provided by frames and events, resulting in complex training strategies and increased computational costs. To address these challenges, we propose an efficient hybrid framework for image semantic segmentation, comprising a Spiking Neural Network branch for events and an Artificial Neural Network branch for frames. Specifically, we introduce three specialized modules to facilitate the interaction between these two branches: the Adaptive Temporal Weighting (ATW) Injector, the Event-Driven Sparse (EDS) Injector, and the Channel Selection Fusion (CSF) module. The ATW Injector dynamically integrates temporal features from event data into frame features, enhancing segmentation accuracy by leveraging critical dynamic temporal information. The EDS Injector effectively combines sparse event data with rich frame features, ensuring precise temporal and spatial information alignment. The CSF module selectively merges these features to optimize segmentation performance. Experimental results demonstrate that our framework not only achieves state-of-the-art accuracy across the DDD17-Seg, DSEC-Semantic, and M3ED-Semantic datasets but also significantly reduces energy consumption, achieving a 65% reduction on the DSEC-Semantic dataset.
Learning Causal Transition Matrix for Instance-dependent Label Noise
Authors: Jiahui Li; Tai-Wei Chang; Kun Kuang; Ximing Li; Long Chen; Jun Zhou
Venue: AAAI 2025 (main)

Abstract: Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of learning with noise, the transition matrix plays a crucial role in the design of statistically consistent algorithms. However, the transition matrix is often considered unidentifiable. One strand of methods typically addresses this problem by assuming that the transition matrix is instance-independent; that is, the probability of mislabeling a particular instance is not influenced by its characteristics or attributes. This assumption is clearly invalid in complex real-world scenarios. To better understand the transition relationship and relax this assumption, we propose to study the data generation process of noisy labels from a causal perspective. We discover that an unobservable latent variable can affect either the instance itself, the label annotation procedure, or both, which complicates the identification of the transition matrix. To address various scenarios, we have unified these observations within a new causal graph. In this graph, the input instance is divided into a noise-resistant component and a noise-sensitive component based on whether they are affected by the latent variable. These two components contribute to identifying the “causal
transition matrix”, which approximates the true transition matrix with theoretical guarantee. In line with this, we have designed a novel training framework that explicitly models this causal relationship and, as a result, achieves a more accurate model for inferring the clean label.
HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning
Authors: Jianfeng Li; Jiawen Zhang; Feng Wang; Lianbo Ma
Venue: AAAI 2025 (main)

Abstract: One-shot methods have significantly advanced the field of neural architecture search (NAS) by adopting weight-sharing strategy to reduce search costs. However, the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the entire supernet into individual sub-supernets by splitting edge by edge to alleviate this issue, yet neglect relationships among edges and result in performance degradation on huge search space. In this paper, we introduce HEP-NAS, a hierarchy-wise partition algorithm designed to further enhance accuracy. To begin with, HEP-NAS treats edges sharing the same end node as a hierarchy, permuting and splitting edges within the same hierarchy to directly search for the optimal operation combination for each intermediate node. This approach aligns more closely with the ultimate goal of NAS. Furthermore, HEP-NAS selects the most promising sub-supernet after each segmentation, progressively narrowing the search space in which the optimal architecture may exist. To improve performance evaluation of sub-supernets, HEP-NAS employs search space mutual distillation, stabilizing the training process and accelerating the convergence of each individual sub-supernet. Within a given budget, HEP-NAS enables the splitting of all edges and gradually searches for architectures with higher accuracy. Experimental results across various datasets and search spaces demonstrate the superiority of HEP-NAS compared to state-of-the-art methods.
Modeling All Response Surfaces in One for Conditional Search Spaces
Authors: Jiaxing Li; Wei Liu; Chao Xue; Yibing Zhan; Xiaoxing Wang; Weifeng Liu; Dacheng Tao
Venue: AAAI 2025 (main)

Abstract: Bayesian Optimization (BO) is a sample-efficient black-box optimizer commonly used in search spaces where hyperparameters are independent. However, in many practical AutoML scenarios, there will be dependencies among hyperparameters, forming a conditional search space, which can be partitioned into structurally distinct subspaces. The structure and dimensionality of hyperparameter configurations vary across these subspaces, challenging the application of BO. Some previous BO works have proposed solutions to develop multiple Gaussian Process models in these subspaces. However, these approaches tend to be inefficient as they require a substantial number of observations to guarantee each GP's performance and cannot capture relationships between hyperparameters across different subspaces. To address these issues, this paper proposes a novel approach to model the response surfaces of all subspaces in one, which can model the relationships between hyperparameters elegantly via a self-attention mechanism. Concretely, we design a structure-aware hyperparameter embedding to preserve the structural information. Then, we introduce an attention-based deep feature extractor, capable of projecting configurations with different structures from various subspaces into a unified feature space, where the response surfaces can be formulated using a single standard Gaussian Process. The empirical results on a simulation function, various real-world tasks, and HPO-B benchmark demonstrate that our proposed approach improves the efficacy and efficiency of BO within conditional search spaces.
Multi-Label Ranking Loss Minimization for Matrix Completion
Authors: Jiaxuan Li; Xiaoyan Zhu; Hongrui Wang; Yu Zhang; Xin Lai; Jiayin Wang
Venue: AAAI 2025 (main)

Abstract: The common matrix completion methods minimize the rank of the matrix to be completed in addition to the Hamming loss between the incomplete and completed matrices. The rank of matrix measures the linear relation among the vectors of matrix, which may introduce ambiguity for data recovery. To cope with this issue, we extend multi-label ranking loss into matrix completion, and employ multi-label ranking loss minimization (MLRM) in this paper to exploit the relative correlation among matrix vectors. In MLRM, the original incomplete matrix is converted into a pairwise ranking matrix, and the approximation on this newly generated matrix can be viewed as a surrogate of multi-label ranking loss to replace the Hamming loss pattern in the existing methods. Extensive experiments demonstrate that MLRM outperforms the state-of-the-art matrix completion methods in varies of applications, including movie recommendation, drug-target interaction prediction and multi-label learning.
Destroy and Repair Using Hyper-Graphs for Routing
Authors: Ke Li; Fei Liu; Zhenkun Wang; Qingfu Zhang
Venue: AAAI 2025 (main)

Abstract: Recent advancements in Neural Combinatorial Optimization (NCO) have shown promise in solving routing problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) without handcrafted designs. Research in this domain has explored two primary categories of methods: iterative and non-iterative. While non-iterative methods struggle to generate near-optimal solutions directly, iterative methods simplify the task by learning local search steps. However, existing iterative methods are often limited by restricted neighborhood searches, leading to suboptimal results. To address this limitation, we propose a novel approach that extends the search to larger neighborhoods by learning a destroy-and-repair strategy. Specifically, we introduce a Destroy-and-Repair framework based on Hyper-Graphs (DRHG). This framework reduces consecutive intact edges to hyper-edges, allowing the model to pay more attention to the destroyed part and decrease the complexity of encoding all nodes. Experiments demonstrate that DRHG achieves state-of-the-art performance on TSP with up to 10,000 nodes and shows strong generalization to real-world TSPLib and CVRPLib problems.
An Efficient and Accurate Dynamic Sparse Training Framework Based on Parameter-Freezing
Authors: Lei Li; Haochen Yang; Jiacheng Guo; Hongkai Yu; Minghai Qin; Tianyun Zhang
Venue: AAAI 2025 (main)

Abstract: Federated learning is a decentralized machine learning approach that consists of servers and clients. It protects data privacy during model training by keeping the training data locally in each client. However, the requirement for the server and clients to frequently synchronize the parameters of the model brings a heavy burden to the communication links, especially when the model size has grown drastically in recent years.  Several methods have been proposed to compress the model size by sparsification to reduce the communication overhead, albeit with significant accuracy degradation. In this work, we propose methods to better trade-off between model accuracy and training efficiency in federated learning. Our first proposed method is a novel sparse mask readjustment rule on the server and the second is a parameter-freezing method during training on the clients. Experimental results show that the model accuracy has significantly improved when combining our proposed methods. For example, compared with the previous state-of-the-art methods with the same total amount of communication cost and computation FLOPs, the accuracy increases on average by 4% and 6% in our methods for CIFAR-10 and CIFAR-100 datasets on ResNet-18, respectively. On the other hand, when targeting the same accuracy, the proposed method can reduce the communication cost by 4-8 times for different datasets with different sparsity levels.
Adaptive Decision Boundary for Few-Shot Class-Incremental Learning
Authors: Linhao Li; Yongzhang Tan; Siyuan Yang; Hao Cheng; Yongfeng Dong; Liang Yang
Venue: AAAI 2025 (main)

Abstract: Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from a limited set of training samples without forgetting knowledge of previously learned classes. Conventional FSCIL methods typically build a robust feature extractor during the base training session with abundant training samples and subsequently freeze this extractor, only fine-tuning the classifier in subsequent incremental phases. However, current strategies primarily focus on preventing catastrophic forgetting, considering only the relationship between novel and base classes, without paying attention to the specific decision spaces of each class. To address this challenge, we propose a plug-and-play Adaptive Decision Boundary Strategy (ADBS), which is compatible with most FSCIL methods. Specifically, we assign a specific decision boundary to each class and adaptively adjust these boundaries during training to optimally refine the decision
spaces for the classes in each session. Furthermore, to amplify the distinctiveness between classes, we employ a novel inter-class constraint loss that optimizes the decision boundaries and prototypes for each class. Extensive experiments on three benchmarks, namely CIFAR100, miniImageNet, and CUB200, demonstrate that incorporating our ADBS method with existing FSCIL techniques significantly improves performance, achieving overall state-of-the-art results.
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic
Authors: Mengxian Li; Qi Wang; Yongjun Xu
Venue: AAAI 2025 (main)

Abstract: The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and widely applied. However, as the number of agents increases, the inherent limitations of these frameworks significantly degrade the performance metrics, such as win rate, total reward, etc. To reduce the influence of the increasing number of agents on the performance metrics, we propose a novel training paradigm of grouped training decentralized execution (GTDE). This framework eliminates the need for a centralized module and relies solely on local information, effectively meeting the training requirements of large-scale multi-agent systems. Specifically, we first introduce an adaptive grouping module, which divides each agent into different groups based on their observation history. To implement end-to-end training, GTDE uses Gumbel-Sigmoid for efficient point-to-point sampling on the grouping distribution while ensuring gradient backpropagation. To adapt to the uncertainty in the number of members in a group, two methods are used to implement a group information aggregation module that merges member information within the group. Empirical results show that in a cooperative environment with 495 agents, GTDE increased the total reward by an average of 382% compared to the baseline. In a competitive environment with 64 agents, GTDE achieved a 100% win rate against the baseline.