bluuebunny/crossref_metadata_2025 huggingface dataset:
Federated Multi-View Clustering via Tensor Factorization
Authors: Feng Wei , Wu Zhenwei , Wang Qianqian , Dong Bo , Tao Zhiqiang , Gao Quanxue
Published: July 2024

Multi-view clustering is an effective method to process massive unlabeled multi-view data. Since data of different views may be collected and held by different parties, it becomes impractical to train a multi-view clustering model in a centralized way, for the sake of privacy. However, federated multi-view clustering is challenging because multi-view learning has to consider the complementary and consistent information between each view distributed across different clients. For another, efficiency is highly expected in federated scenarios. Therefore, we propose a novel federated multi-view clustering method with tensor factorization (TensorFMVC), which is built based on K-means and hence is more efficient. Besides, TensorFMVC avoids initializing centroids to address the performance degradation of K-means due to its sensitivity to centroid initialization. A three-order tensor stacked by cluster assignment matrices is introduced to exploit the complementary information and spatial structure of different views. Furthermore, we divide the optimization into several subproblems and develop a federated optimization approach to support cooperative model training. Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering.
Online Combinatorial Optimization with Group Fairness Constraints
Authors: Golrezaei Negin , Niazadeh Rad , Patel Kumar Kshitij , Susan Fransisca
Published: July 2024

As digital marketplaces and services continue to expand, it is crucial to maintain a safe and fair environment for all users. This requires implementing fairness constraints into the sequential decision-making processes of these platforms to ensure equal treatment. However, this can be challenging as these processes often need to solve NP-complete problems with exponentially large decision spaces at each time step. To overcome this, we propose a general framework incorporating robustness and fairness into NP-complete problems, such as optimizing product ranking and maximizing sub-modular functions. Our framework casts the problem as a max-min game between a primal player aiming to maximize the platform's objective and a dual player in charge of group fairness constraints. We show that one can trace the entire Pareto fairness curve by changing the thresholds on the fairness constraints. We provide theoretical guarantees for our method and empirically evaluate it, demonstrating its effectiveness.
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
Authors: Gao Chengqian , de Vazelhes William , Zhang Hualin , Gu Bin , Xu Zhiqiang
Published: July 2024

Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably, they shine in scenarios with imperfect reward functions, making them invaluable for real-world applications where dense reward signals may be elusive. Yet, an inherent assumption in ES—that all input features are task-relevant—poses challenges, especially when confronted with irrelevant features common in real-world problems. This work scrutinizes this limitation, particularly focusing on the Natural Evolution Strategies (NES) variant. We propose NESHT, a novel approach that integrates Hard-Thresholding (HT) with NES to champion sparsity, ensuring only pertinent features are employed. Backed by rigorous analysis and empirical tests, NESHT demonstrates its promise in mitigating the pitfalls of irrelevant features and shines in complex decision-making problems like noisy Mujoco and Atari tasks. Our code is available at https://github.com/cangcn/NES-HT.
ParsNets: A Parsimonious Composition of Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning
Authors: Guo Jingcai , Zhou Qihua , Lu Xiaocheng , Li Ruibin , Liu Ziming , Zhang Jie , Han Bo , Chen Junyang , Xie Xin , Guo Song
Published: July 2024

This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, in which we are interested in learning a composition of on-device friendly linear networks, each with orthogonality and low-rankness properties, to achieve equivalent or better performance against deep models. Concretely, we first refactor the core module of ZSL, i.e., the visual-semantics mapping function, into several base linear networks that correspond to diverse components of the semantic space, wherein the complex nonlinearity can be collapsed into simple local linearities. Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes. To enhance the model's adaptability and counterbalance the over-/under-fittings, a set of sample-wise indicators is employed to select a sparse subset from these base linear networks to form a composite semantic predictor for each sample. Notably, maximal margin geometry can guarantee the diversity of features and, meanwhile, local linearities guarantee efficiency. Thus, our ParsNets can generalize better to unseen classes and can be deployed flexibly on resource-constrained devices.
Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection
Authors: Guo Xianjie , Yu Kui , Wang Hao , Cui Lizhen , Yu Han , Li Xiaoxiao
Published: July 2024

Federated causal discovery (FCD) aims to uncover causal relationships among variables from decentralized data across multiple clients, while preserving data privacy. In practice, the sample quality of each client's local data may vary across different variable spaces, referred to as sample quality heterogeneity. Thus, data from different clients might be suitable for learning different causal relationships among variables. Model aggregated under existing FCD methods requires the entire model parameters from each client, thereby being unable to handle the sample quality heterogeneity issue. In this paper, we propose the Federated Adaptive Causal Discovery (FedACD) method to bridge this gap. During federated model aggregation, it adaptively selects the causal relationships learned under the "good" variable space (i.e., one with high-quality samples) from each client, while masking those learned under the "bad" variable space (i.e., one with low-quality samples). This way, each client only needs to send the optimal learning results to the server, achieving accurate FCD. Extensive experiments on various types of datasets demonstrate significant advantages of FedACD over existing methods. The source code is available at https://github.com/Xianjie-Guo/FedACD.
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Authors: Han Qi , Tian Zhibo , Xia Chengwei , Zhan Kun
Published: July 2024

Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in fully exploiting the potential of unlabeled data. To address this, we employ information entropy neural estimation to utilize the potential of unlabeled samples. Inspired by contrastive learning, the entropy is estimated by maximizing a lower bound on mutual information across different augmented views. Moreover, we theoretically analyze that the information entropy of the posterior of an image classifier is approximated by maximizing the likelihood function of the softmax predictions. Guided by these insights, we optimize our model from both perspectives to ensure that the predicted probability distribution closely aligns with the ground-truth distribution. Given the theoretical connection to information entropy, we name our method InfoMatch. Through extensive experiments, we show its superior performance. The source code is available at https://github.com/kunzhan/InfoMatch.
Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains
Authors: Hu Ke , Xiang Liyao , Tang Peng , Qiu Weidong
Published: July 2024

Federated learning (FL) is a machine learning paradigm that aggregates knowledge and utilizes computational power from multiple participants to train a global model. However, a commonplace challenge—non-independent and identically distributed (non-i.i.d.) data across participants—can lead to significant divergence in model updates, thus diminishing training efficacy. In this paper, we propose the Feature Norm Regularized Federated Learning (FNR-FL) algorithm to tackle the non-i.i.d challenge. FNR-FL incorporates class average feature norms into the loss function by a straightforward yet effective regularization strategy. The core idea of FNR-FL is to penalize the deviations in the update directions of local models caused by the non-i.i.d data. Theoretically, we provide convergence guarantees for FNR-FL when training under non-i.i.d scenarios. Practically, our comprehensive experimental evaluations demonstrate that FNR-FL significantly outperforms existing FL algorithms in terms of test accuracy, and maintains a competitive convergence rate with lower communication overhead and shorter duration. Compared to FedAvg, FNR-FL exhibits a 66.24% improvement in accuracy and an 11.40% reduction in training time, underscoring its enhanced effectiveness and efficiency. The code is available on GitHub at: https://github.com/LonelyMoonDesert/FNR-FL.
Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis
Authors: Ji Zhoulin , Lin Chenhao , Wang Hang , Shen Chao
Published: July 2024

Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively covering authentic, synthetic, and partially forged speech samples that include multiple segments synthesized by different high-quality algorithms. Moreover, we propose a TEmporal Speech LocalizaTion network, called TEST, aiming at simultaneously performing authenticity detection, multiple fake segments localization, and synthesis algorithms recognition, without any complex post-processing. TEST effectively integrates LSTM and Transformer to extract more powerful temporal speech representations and utilizes dense prediction on multi-scale pyramid features to estimate the synthetic spans. Our model achieves an average mAP of 83.55% and an EER of 5.25% at the utterance level. At the segment level, it attains an EER of 1.07% and a 92.19% F1 score. These results highlight the model's robust capability for a comprehensive analysis of synthetic speech, offering a promising avenue for future research and practical applications in this field.
CAP: A Context-Aware Neural Predictor for NAS
Authors: Ji Han , Feng Yuqi , Sun Yanan
Published: July 2024

Neural predictors are effective in boosting the time-consuming performance evaluation stage in neural architecture search (NAS), owing to their direct estimation of unseen architectures. Despite the effectiveness, training a powerful neural predictor with fewer annotated architectures remains a huge challenge. In this paper, we propose a context-aware neural predictor (CAP) which only needs a few annotated architectures for training based on the contextual information from the architectures. Specifically, the input architectures are encoded into graphs and the predictor infers the contextual structure around the nodes inside each graph. Then, enhanced by the proposed context-aware self-supervised task, the pre-trained predictor can obtain expressive and generalizable representations of architectures. Therefore, only a few annotated architectures are sufficient for training. Experimental results in different search spaces demonstrate the superior performance of CAP compared with state-of-the-art neural predictors. In particular, CAP can rank architectures precisely at the budget of only 172 annotated architectures in NAS-Bench-101. Moreover, CAP can help find promising architectures in both NAS-Bench-101 and DARTS search spaces on the CIFAR-10 dataset, serving as a useful navigator for NAS to explore the search space efficiently.
EFEVD: Enhanced Feature Extraction for Smart Contract Vulnerability Detection
Authors: Jiang Chi , Liu Xihan , Wang Shenao , Liu Jinzhuo , Zhang Yin
Published: July 2024

Because of the wide deployment of smart contracts, smart contract vulnerabilities pose a challenging risk to blockchain security. Currently, deep learning-based vulnerability detection is a very attractive solution due to its ability to identify complex patterns and features. The existing methods mainly consider the contract code content features, expert knowledge patterns, and contract code modalities. To further enhance smart contract vulnerability detection, this paper attempts to identify community features from smart contracts with similar semantic and syntactic structures, and shared features from two related vulnerability detection tasks, vulnerability classification and localization. The experimental results verify that the proposed approach significantly outperforms the state-of-the-art methods in terms of accuracy, recall, precision, and F1-score.
HyQ: Hardware-Friendly Post-Training Quantization for CNN-Transformer Hybrid Networks
Authors: Kim Nam Joon , Lee Jongho , Kim Hyun
Published: July 2024

Hybrid models that combine CNNs and ViTs have recently emerged as state-of-the-art computer vision models. To efficiently deploy these hybrid models on resource-constrained mobile/edge devices, quantization is emerging as a promising solution. However, post-training quantization (PTQ), which does not require retraining or labeled data, has not been extensively studied for hybrid models. In this study, we propose a novel PTQ technique specialized for CNN-transformer hybrid models by considering the hardware design of hybrid models on AI accelerators such as GPUs and FPGAs. First, we introduce quantization-aware distribution scaling to address the large outliers caused by inter-channel variance in convolution layers. Furthermore, in the transformer block, we propose approximating the integer-only softmax with a linear function. This approach allows us to avoid costly FP32/INT32 multiplications, resulting in more efficient computations. Experimental results show that the proposed quantization method with INT8 precision demonstrated a 0.39% accuracy drop compared with the FP32 baseline on MobileViT-s with the ImageNet-1k dataset. Furthermore, when implemented on the FPGA platform, the proposed linear softmax achieved significant resource savings, reducing the look-up table and flip-flop usage by 1.8 ~ 2.1x and 1.3 ~ 1.9x, respectively, compared with the existing second-order polynomial approximation. The code is available at https://github.com/IDSL-SeoulTech/HyQ.
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
Authors: Kim Woo Kyung , Yoo Minjong , Woo Honguk
Published: July 2024

Data-driven offline reinforcement learning and imitation learning approaches have been gaining popularity in addressing sequential decision-making problems. Yet, these approaches rarely consider learning Pareto-optimal policies from a limited pool of expert datasets. This becomes particularly marked due to practical limitations in obtaining comprehensive datasets for all preferences, where multiple conflicting objectives exist and each expert might hold a unique optimization preference for these objectives. In this paper, we adapt inverse reinforcement learning (IRL) by using reward distance estimates for regularizing the discriminator. This enables progressive generation of a set of policies that accommodate diverse preferences on the multiple objectives, while using only two distinct datasets, each associated with a different expert preference. In doing so, we present a Pareto IRL framework (ParIRL) that establishes a Pareto policy set from these limited datasets. In the framework, the Pareto policy set is then distilled into a single, preference-conditioned diffusion model, thus allowing users to immediately specify which expert's patterns they prefer. Through experiments, we show that ParIRL outperforms other IRL algorithms for various multi-objective control tasks, achieving the dense approximation of the Pareto frontier. We also demonstrate the applicability of ParIRL with autonomous driving in CARLA.
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Authors: Komanduri Aneesh , Wu Yongkai , Chen Feng , Wu Xintao
Published: July 2024

Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior learned from auxiliary labels and the latent causal structure. We theoretically show the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation.
Protecting Object Detection Models from Model Extraction Attack via Feature Space Coverage
Authors: Li Zeyu , Pu Yuwen , Zhang Xuhong , Li Yu , Li Jinbao , Ji Shouling
Published: July 2024

The model extraction attack is an attack pattern aimed at stealing well-trained machine learning models' functionality or privacy information. With the gradual popularization of AI-related technologies in daily life, various well-trained models are being deployed. As a result, these models are considered valuable assets and attractive to model extraction attackers. Currently, the academic community primarily focuses on defense for model extraction attacks in the context of classification, with little attention to the more commonly used task scenario of object detection. Therefore, we propose a detection framework targeting model extraction attacks against object detection models in this paper. The framework first locates suspicious users based on feature coverage in query traffic and uses an active verification module to confirm whether the identified suspicious users are attackers. Through experiments conducted in multiple task scenarios, we validate the effectiveness and detection efficiency of the proposed method.
A De-singularity Subgradient Approach for the Extended Weber Location Problem
Authors: Lai Zhao-Rong , Wu Xiaotian , Fang Liangda , Chen Ziliang
Published: July 2024

The extended Weber location problem is a classical optimization problem that has inspired some new works in several machine learning scenarios recently. However, most existing algorithms may get stuck due to the singularity at the data points when the power of the cost function 1\<= q<2, such as the widely-used iterative Weiszfeld approach. In this paper, we establish a de-singularity subgradient approach for this problem. We also provide a complete proof of convergence which has fixed some incomplete statements of the proofs for some previous Weiszfeld algorithms. Moreover, we deduce a new theoretical result of superlinear convergence for the iteration sequence in a special case where the minimum point is a singular point. We conduct extensive experiments in a real-world machine learning scenario to show that the proposed approach solves the singularity problem, produces the same results as in the non-singularity cases, and shows a reasonable rate of linear convergence. The results also indicate that the q-th power case (1<q<2) is more advantageous than the 1-st power case and the 2-nd power case in some situations. Hence the de-singularity subgradient approach is beneficial to advancing both theory and practice for the extended Weber location problem.
Unbiased Active Semi-supervised Binary Classification Models
Authors: Lee JooChul , Ma Weidong , Wang Ziyang
Published: July 2024

Active learning is known to be a well-motivated algorithm that aims to maximize model performance with relatively small data, but it introduces sampling bias due to active selection. To adjust the bias, current literature utilizes corrective weights in a supervised learning approach. However, those methods consider only a small amount of actively sampled data and thus estimation efficiency can be improved using unsampled data together. In this paper, we develop an actively improved augmented estimation equation (AI-AEE) based on corrective weights as well as imputation models that allow us to leverage unlabeled data. The asymptotic distribution of the proposed estimator as the solution to the AI-AEE is derived, and an optimal sampling scheme to minimize the asymptotic mean squared error of the estimator is proposed. We then propose a general practical algorithm for training prediction models in the active and semi-supervised learning framework. The superiority of our method is demonstrated on synthetic and real data examples.
Combinatorial Routing for Neural Trees
Authors: Li Jiahao , Cai Ruichu , Yan Yuguang
Published: July 2024

Neural trees benefit from the high-level representation of neural networks and the interpretability of decision trees. Therefore, the existing works on neural trees perform outstandingly on various tasks such as architecture search. However, these works require every router to provide only one successor for each sample, causing the predictions to be dominated by the elite branch and its derivative architectures. To break this branch dominance, we propose the combinatorial routing neural tree method, termed CombRo. Unlike the previous methods employing unicast routing, CombRo performs multicast schema in each iteration, allowing the features to be routed to any combination of successors at every non-leaf. The weights of each architecture are then evaluated accordingly. We update the weights by training the routing subnetwork, and the architecture with the top weight is selected in the final step. We compare CombRo with the existing algorithms on 3 public image datasets, demonstrating its superior performance in terms of accuracy. Visualization results further validate the effectiveness of the multicast routing schema. Code is available at https://github.com/JiahaoLi-gdut/CombRo.
DarkFed: A Data-Free Backdoor Attack in Federated Learning
Authors: Li Minghui , Wan Wei , Ning Yuxuan , Hu Shengshan , Xue Lulu , Zhang Leo Yu , Wang Yichen
Published: July 2024

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development.

To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL.

Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset.

Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed.
SketchEdit: Editing Freehand Sketches at the Stroke-Level
Authors: Li Tengjie , Tu Shikui , Xu Lei
Published: July 2024

Recent sketch synthesis methods have demonstrated the capability of generating lifelike outcomes. However, these methods directly encode the entire sketches making it challenging to decouple the strokes from the sketches and have difficulty in controlling local sketch synthesis, e.g., stroke editing. Besides, the sketch editing task encounters the issue of accurately positioning the edited strokes, because users may not be able to draw on the exact position and the same stroke may appear in various locations in different sketches. We propose SketchEdit to realize flexible editing of sketches at the stroke-level for the first time. To tackle the challenge of decoupling strokes, SketchEdit divides a drawing sequence of a sketch into a series of strokes based on the pen state, aligns the stroke segments to have the same starting position, and learns the embeddings of every stroke by a proposed stroke encoder. Moreover, we overcome the problem of stroke placement via a diffusion process, which progressively generates the locations for the strokes to be synthesized, using the stroke features as the guiding condition. Experiments demonstrate that SketchEdit is effective for stroke-level sketch editing and sketch reconstruction. The source code is publicly available at https://github.com/CMACH508/SketchEdit/.
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition
Authors: Cope Dylan , McBurney Peter
Published: July 2024

In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community. This observation led to research into Zero-Shot Coordination (ZSC) for learning communication strategies that are robust to agents not encountered during training. However, ZSC typically assumes that no prior data is available about the agents that will be encountered in the zero-shot setting. In many cases, this presents an unnecessarily hard problem and rules out communication via preestablished conventions. We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions are relaxed by allowing a 'joiner' agent to learn from a dataset of interactions between agents in a target community. We propose and compare two methods for solving CLAPs: Behaviour Cloning (BC), and Emergent Communication pretraining and Translation Learning (ECTL), in which an agent is trained in self-play with EC and then learns to translate between an emergent protocol and the target community's protocol.
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
Authors: Liu Yonghao , Li Mengyu , Liang Di , Li Ximing , Giunchiglia Fausto , Huang Lan , Feng Xiaoyue , Guan Renchu
Published: July 2024

Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information, which can hinder a complete understanding of the intended meaning of the sentences due to the ambiguity and vagueness of language. To address this challenge, we propose an innovative ScenaFuse adapter that simultaneously integrates large-scale pre-trained linguistic knowledge and relevant visual information for NLI tasks. Specifically, we first design an image-sentence interaction module to incorporate visuals into the attention mechanism of the pre-trained model, allowing the two modalities to interact comprehensively. Furthermore, we introduce an image-sentence fusion module that can adaptively integrate visual information from images and semantic information from sentences. By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks. Extensive benchmark experiments demonstrate that our proposed ScenaFuse, a scenario-guided approach, consistently boosts NLI performance.
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
Authors: Meerza Syed Irfan Ali , Liu Jian
Published: July 2024

Federated Learning (FL) is a technique that allows multiple parties to train a shared model collaboratively without disclosing their private data. It has become increasingly popular due to its distinct privacy advantages. However, FL models can suffer from biases against certain demographic groups (e.g., racial and gender groups) due to the heterogeneity of data and party selection. Researchers have proposed various strategies for characterizing the group fairness of FL algorithms to address this issue. However, the effectiveness of these strategies in the face of deliberate adversarial attacks has not been fully explored. Although existing studies have revealed various threats (e.g., model poisoning attacks) against FL systems caused by malicious participants, their primary aim is to decrease model accuracy, while the potential of leveraging poisonous model updates to exacerbate model unfairness remains unexplored. In this paper, we propose a new type of model poisoning attack, EAB-FL, with a focus on exacerbating group unfairness while maintaining a good level of model utility. Extensive experiments on three datasets demonstrate the effectiveness and efficiency of our attack, even with state-of-the-art fairness optimization algorithms and secure aggregation rules employed. We hope this work will help the community fully understand the attack surfaces of current FL systems and facilitate corresponding mitigation to improve their resilience.
LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game
Authors: Lu Jianfeng , Chen Yue , Cao Shuqin , Chen Longbiao , Wang Wei , Xin Yun
Published: July 2024

Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to alleviate communication burdens, its model performance will be degraded by non-IID data and limited communication resources. Current works often assume that data is uniformly distributed, which however contradicts the heterogeneity of IoT. Solutions involving additional model training to check the data distribution inevitably increase computational costs and the risk of privacy leakage. The challenges in solving these issues are how to reduce the impact of non-IID data without involving raw data, and how to rationalize the communication resource allocation for addressing straggler problem. To tackle these challenges, we propose a novel optimization method based on coaLition formation gamE and grAdient Projection, called LEAP. Specifically, we combine edge data distribution with coalition formation game innovatively to adjust the correlations between clients and ESs dynamically, ensuring optimal correlations. We further capture the client heterogeneity to achieve the rational bandwidth allocation from coalition perception and determine the optimal transmission power within specified delay constraints at the client level. Experimental results on four real datasets show that LEAP is able to achieve 20.62% improvement in model accuracy compared to the state-of-the-art baselines. Moreover, LEAP effectively reduces transmission energy consumption by at least about 2.24 times.
Common-Individual Semantic Fusion for Multi-View Multi-Label Learning
Authors: Lyu Gengyu , Kang Weiqi , Wang Haobo , Li Zheng , Yang Zhen , Feng Songhe
Published: July 2024

In Multi-View Multi-Label Learning, each instance is described by several heterogeneous features and associated with multiple valid labels simultaneously. Existing methods mainly focus on leveraging feature-level view fusion to capture a common representation for multi-label classifier induction. In this paper, we take a new perspective and propose a new semantic-level fusion model named Common-Individual Semantic Fusion Multi-View Multi-Label Learning Method (CISF). Different from previous feature-level fusion model, our proposed method directly focuses on semantic-level view fusion and simultaneously take both the common semantic across different views and the individual semantic of each specific view into consideration. Specifically, we first assume each view involves some common semantic labels while owns a few exclusive semantic labels. Then, the common and exclusive semantic labels are separately forced to be consensus and diverse to excavate the consistences and complementarities among different views. Afterwards, we introduce the low-rank and sparse constraint to highlight the label co-occurrence relationship of common semantics and the view-specific expression of individual semantics. We provide theoretical guarantee for the strict convexity of our method by properly setting parameters. Extensive experiments on various data sets have verified the superiority of our method.
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
Authors: Ma Jiajun , Hu Tianyang , Wang Wenjia
Published: July 2024

Self-supervised learning (SSL) aims to learn the intrinsic features of data without labels. Despite the diverse SSL architectures, the projection head always plays an important role in improving downstream task performance. In this study, we systematically investigate the role of the projection head in SSL. We find that the projection head targets the uniformity aspect, which maps samples into uniform distribution and enables the encoder to focus on extracting semantic features. Drawing on this insight, we propose a Representation Evaluation Design (RED) in SSL models in which a shortcut connection between the representation and the projection vectors is built. Our extensive experiments with different architectures (including SimCLR, MoCo-V2, and SimSiam) on various datasets demonstrate that the RED-SSL consistently outperforms their baseline counterparts in downstream tasks. Furthermore, the RED-SSL learned representations exhibit superior robustness to previously unseen augmentations and out-of-distribution data.
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Authors: Ning Zhiyuan , Tian Chunlin , Xiao Meng , Fan Wei , Wang Pengyang , Li Li , Wang Pengfei , Zhou Yuanchun
Published: July 2024

Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short of addressing these complexities holistically. In response, we propose FedGCS, a novel generative client selection framework that innovatively recasts the client selection process as a generative task. Drawing inspiration from the methodologies used in large language models, FedGCS efficiently encodes abundant decision-making knowledge within a continuous representation space, enabling efficient gradient-based optimization to search for optimal client selection that will be finally output via generation. The framework comprises four steps: (1) automatic collection of diverse “selection-score” pair data using classical client selection methods; (2) training an encoder-evaluator-decoder framework on this data to construct a continuous representation space; (3) employing gradient-based optimization in this space for optimal client selection; (4) generating the final optimal client selection via using beam search for the well-trained decoder. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. The effectiveness of FedGCS is proven through extensive experimental analyses.
What Makes Models Compositional? A Theoretical View
Authors: Ram Parikshit , Klinger Tim , Gray Alexander G.
Published: July 2024

Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of existing sequence processing models. These benchmarks often highlight failures of existing models, but it is not clear why these models fail in this way. In this paper, we seek to theoretically understand the role the compositional structure of the models plays in these failures and how this structure relates to their expressivity and sample complexity. We propose a general neuro-symbolic definition of compositional functions and their compositional complexity. We then show how various existing general and special purpose sequence processing models (such as recurrent, convolution and attention-based ones) fit this definition and use it to analyze their compositional complexity. Finally, we provide theoretical guarantees for the expressivity and systematic generalization of compositional models that explicitly depend on our proposed definition and highlighting factors which drive poor empirical performance.
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning
Authors: Schlechtinger Michael , Kosack Damaris , Krause Franz , Paulheim Heiko
Published: July 2024

In the rapidly evolving landscape of eCommerce, Artificial Intelligence (AI) based pricing algorithms, particularly those utilizing Reinforcement Learning (RL), are becoming increasingly prevalent. This rise has led to an inextricable pricing situation with the potential for market collusion. Our research employs an experimental oligopoly model of repeated price competition, systematically varying the environment to cover scenarios from basic economic theory to subjective consumer demand preferences. We also introduce a novel demand framework that enables the implementation of various demand models, allowing for a weighted blending of different models.

In contrast to existing research in this domain, we aim to investigate the strategies and emerging pricing patterns developed by the agents, which may lead to a collusive outcome. Furthermore, we investigate a scenario where agents cannot observe their competitors’ prices. Finally, we provide a comprehensive legal analysis across all scenarios.

Our findings indicate that RL-based AI agents converge to a collusive state characterized by the charging of supracompetitive prices, without necessarily requiring inter-agent communication. Implementing alternative RL algorithms, altering the number of agents or simulation settings, and restricting the scope of the agents’ observation space does not significantly impact the collusive market outcome behavior.
Robust Contrastive Multi-view Kernel Clustering
Authors: Su Peng , Liu Yixi , Li Shujian , Huang Shudong , Lv Jiancheng
Published: July 2024

Multi-view kernel clustering (MKC) aims to fully reveal the consistency and complementarity of multiple views in a potential Hilbert space, thereby enhancing clustering performance. The clustering results of most MKC methods are highly sensitive to the quality of the constructed kernels, as traditional methods independently compute kernel matrices for each view without fully considering complementary information across views. In previous contrastive multi-view kernel learning, the goal was to bring cross-view instances of the same sample closer during the kernel construction process while pushing apart instances across samples to achieve a comprehensive integration of cross-view information. However, its inherent drawback is the potential inappropriate amplification of distances between different instances of the same clusters (i.e., false negative pairs) during the training process, leading to a reduction in inter-class discriminability. To address this challenge, we propose a Robust Contrastive multi-view kernel Learning approach (R-CMK) against false negative pairs. It partitions negative pairs into different intervals based on distance or similarity, and for false negative pairs, reverses their optimization gradient. This effectively avoids further amplification of distances for false negative pairs while simultaneously pushing true negative pairs farther apart. We conducted comprehensive experiments on various MKC methods to validate the effectiveness of the proposed method. The code is available at https://github.com/Duo-laimi/rcmk_main.
MOSER: Learning Sensory Policy for Task-specific Viewpoint via View-conditional World Model
Authors: Wan Shenghua , Sun Hai-Hang , Gan Le , Zhan De-Chuan
Published: July 2024

Reinforcement learning from visual observations is a challenging problem with many real-world applications. Existing algorithms mostly rely on a single observation from a well-designed fixed camera that requires human knowledge. Recent studies learn from different viewpoints with multiple fixed cameras, but this incurs high computation and storage costs and may not guarantee the coverage of the optimal viewpoint. To alleviate these limitations, we propose a straightforward View-conditional Partially Observable Markov Decision Processes (VPOMDPs) assumption and develop a new method, the MOdel-based SEnsor controlleR (MOSER). MOSER jointly learns a view-conditional world model (VWM) to simulate the environment, a sensory policy to control the camera, and a motor policy to complete tasks. We design intrinsic rewards from the VWM without additional modules to guide the sensory policy to adjust the camera parameters. Experiments on locomotion and manipulation tasks demonstrate that MOSER autonomously discovers task-specific viewpoints and significantly outperforms most baseline methods.
Contrastive and View-Interaction Structure Learning for Multi-view Clustering
Authors: Wang Jing , Feng Songhe
Published: July 2024

Existing Deep Multi-view Clustering (DMVC) approaches typically concentrate on capturing consensus semantics from multiple views, where contrastive learning is widely used to align view-specific representations of each view. Unfortunately, view-specific representations are extracted from the content information of the corresponding instance, neglecting the relationships among different instances. Furthermore, existing contrastive loss imports numerous false negative pairs that conflict with the clustering objectives. In response to these challenges, we propose a contraStive and viEw-interaction stRucture learning framework for multI-viEw cluStering (SERIES). Our method takes into account the structural relations among instances and boosts the contrastive loss to improve intra-class compactness. Meanwhile, a cross-view dual relation generation mechanism is introduced to achieve the consensus structural graph across multiple views for clustering. Specifically, we initially acquire view-specific representations using multiple graph autoencoders to exploit both content information and structural information. Furthermore, to pull together the same cluster instances, a soft negative pair aware contrastive loss is employed to distinguish the dissimilar instances while attracting similar instances. Thereafter, the view-specific representations are fed into cross-view dual relation generation layers to generate the affinity matrices of each other, aiming to reveal a consistent structural graph across various views. Extensive experiments conducted on six benchmarks illustrate the superiority of our method compared to other state-of-the-art approaches.
Self-adaptive Extreme Penalized Loss for Imbalanced Time Series Prediction
Authors: Wang Yiyang , Han Yuchen , Guo Yuhan
Published: July 2024

Forecasting time series in imbalanced data presents a significant research challenge that requires considerable attention. Although there are specialized techniques available to tackle imbalanced time series prediction, existing approaches tend to prioritize extreme predictions at the expense of compromising the forecasting accuracy of normal samples. We in this paper propose an extreme penalized loss function that relaxes the constraint on overestimating extreme events, thereby imposing great penalties on both normal and underestimating extreme events. In addition, we provide a self-adaptive way for setting the hyperparameters of the loss function. Then, both the proposed loss function and an attention module are integrated with LSTM networks in a decomposition-based framework. Extensive experiments conducted on real-world datasets demonstrate the superiority of our framework compared to other state-of-the-art approaches for both time series prediction and block maxima prediction tasks.
Subgraph Pooling: Tackling Negative Transfer on Graphs
Authors: Wang Zehong , Zhang Zheyuan , Zhang Chuxu , Ye Yanfang
Published: July 2024

Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead to reduced performance, known as negative transfer. Unlike in image or text data, we find that negative transfer could commonly occur in graph-structured data, even when source and target graphs have semantic similarities. Specifically, we identify that structural differences significantly amplify the dissimilarities in the node embeddings across graphs. To mitigate this, we bring a new insight in this paper: for semantically similar graphs, although structural differences lead to significant distribution shift in node embeddings, their impact on subgraph embeddings could be marginal. Building on this insight, we introduce Subgraph Pooling (SP) by aggregating nodes sampled from a k-hop neighborhood and Subgraph Pooling++ (SP++) by a random walk, to mitigate the impact of graph structural differences on knowledge transfer. We theoretically analyze the role of SP in reducing graph discrepancy and conduct extensive experiments to evaluate its superiority under various settings. The proposed SP methods are effective yet elegant, which can be easily applied on top of any backbone Graph Neural Networks (GNNs). Our code and data are available at: https://github.com/Zehong-Wang/Subgraph-Pooling.
PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs
Authors: Wei Ping , Liu Menghan , Cen Jianhuan , Zhou Ziyang , Chen Liao , Zou Qingsong
Published: July 2024

The rapid development of neural network (NN) methods for solving partial differential equations (PDEs) has created an urgent need for evaluation and comparison of these methods. In this study, we propose PDENNEval, a comprehensive and systematic evaluation of 12 NN methods for PDEs. These methods are classified into function learning type and operator learning type based on their different mathematical foundations. The evaluation is implemented using a diverse dataset comprising 19 distinct PDE problems selected from various scientific fields such as fluid, materials, finance, and electromagnetic. Several evaluation results are reported, aiming to provide guidance for further research in this field. Our code and data are publicly available at https://github.com/zhouzy36/PDENNEval.