GenAI4ELab/papercli-papers huggingface dataset:
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Authors: Yupei Yang; Biwei Huang; Shikui Tu; Lei Xu
Venue: IJCAI 2024 (main)

Abstract: The effectiveness of model training heavily relies on the quality of available training resources. However, budget constraints often impose limitations on data collection efforts. To tackle this challenge, we introduce causal exploration in this paper, a strategy that leverages the underlying causal knowledge for both data collection and model training. We, in particular, focus on enhancing the sample efficiency and reliability of the world model learning within the domain of task-agnostic reinforcement learning. During the exploration phase, the agent actively selects actions expected to yield causal insights most beneficial for world model training. Concurrently, the causal knowledge is acquired and incrementally refined with the ongoing collection of data. We demonstrate that causal exploration aids in learning accurate world models using fewer data and provide theoretical guarantees for its convergence. Empirical experiments, on both synthetic data and real-world applications, further validate the benefits of causal exploration. The source code is available at https://github.com/CMACH508/CausalExploration.
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Authors: Sin-Yee Yap; Junn Yong Loo; Chee-Ming Ting; Fuad Noman; Raphaël C.-W. Phan; Adeel Razi; David L. Dowe
Venue: IJCAI 2024 (main)

Abstract: Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging the non-Euclidean topology and dynamic aspects of brain connectivity across time. In this paper, a deep spatiotemporal variational Bayes (DSVB) framework is proposed to learn time-varying topological structures in dynamic FC networks for identifying autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture rich spatiotemporal patterns across dynamic FC networks. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework substantially outperforms state-of-the-art methods in identifying patients with ASD. Dynamic FC analyses with DSVB-learned embeddings reveal apparent group differences between ASD and healthy controls in brain network connectivity patterns and switching dynamics of brain states.
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
Authors: Bo Ye; Kai Gan; Tong Wei; Min-Ling Zhang
Venue: IJCAI 2024 (main)

Abstract: In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. Moreover, capturing the semantics of unlabeled novel category samples is also challenging due to the missing label information. To address the above issues, we introduce 1) the adaptive synchronizing marginal loss which imposes class-specific negative margins to alleviate the model bias towards seen classes, and 2) the pseudo-label contrastive clustering which exploits pseudo-labels predicted by the model to group unlabeled data from the same category together in the output space. Extensive experiments on benchmark datasets demonstrate that previous approaches may significantly hinder novel class learning, whereas our method strikingly balances the learning pace between seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset. Importantly, we find that fine-tuning the self-supervised pre-trained model significantly boosts the performance, which is overlooked in prior literature. Our code is available at https://github.com/yebo0216best/LPS-main.
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Authors: Liping Yi; Han Yu; Zhuan Shi; Gang Wang; Xiaoguang Liu; Lizhen Cui; Xiaoxiao Li
Venue: IJCAI 2024 (main)

Abstract: Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client's model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines.
Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes
Authors: Kang You; Kai Liu; Li Yu; Pan Gao; Dandan Ding
Venue: IJCAI 2024 (main)

Abstract: Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces. Another key challenge lies in reducing decoding latency, a crucial requirement in real-world application. In this paper, we propose Pointsoup, an efficient learning-based geometry codec that attains high-performance and extremely low-decoding-latency simultaneously. Inspired by conventional Trisoup codec, a point model-based strategy is devised to characterize local surfaces. Specifically, skin features are embedded from local windows via an attention-based encoder, and dilated windows are introduced as cross-scale priors to infer the distribution of quantized features in parallel. During decoding, features undergo fast refinement, followed by a folding-based point generator that reconstructs point coordinates with fairly fast speed. Experiments show that Pointsoup achieves state-of-the-art performance on multiple benchmarks with significantly lower decoding complexity, i.e., up to 90~160× faster than the G-PCCv23 Trisoup decoder on a comparatively low-end platform (e.g., one RTX 2080Ti). Furthermore, it offers variable-rate control with a single neural model (2.9MB), which is attractive for industrial practitioners.
EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices
Authors: Di Yu; Xin Du; Linshan Jiang; Wentao Tong; Shuiguang Deng
Venue: IJCAI 2024 (main)

Abstract: Deep Spiking Neural Networks (SNNs), as an advanced form of SNNs characterized by their multi-layered structure, have recently achieved significant breakthroughs in performance across various domains. The biological plausibility and energy efficiency of SNNs naturally align with the requisites of edge computing (EC) scenarios, thereby prompting increased interest among researchers to explore the migration of these deep SNN models onto edge devices such as sensors and smartphones. However, the progress of migration work has been notably challenging due to the influence of the substantial increase in model parameters and the demanding computational requirements in practical applications. In this work, we propose a deep SNN splitting framework named EC-SNN to run the intricate SNN models on edge devices. We first partition the full SNN models into smaller sub-models to allocate their model parameters on multiple edge devices. Then, we provide a channel-wise pruning method to reduce the size of each sub-model, thereby further reducing the computational load. We design extensive experiments on six datasets (i.e., four non-neuromorphic and two neuromorphic datasets) to substantiate that our approach can significantly diminish the inference execution latency on edge devices and reduce the overall energy consumption per deployed device with an average reduction of 60.7% and 27.7% respectively while keeping the effectiveness of the accuracy.
Bandits with Concave Aggregated Reward
Authors: Yingqi Yu; Sijia Zhang; Shaoang Li; Lan Zhang; Wei Xie; Xiang-Yang Li
Venue: IJCAI 2024 (main)

Abstract: Multi-armed bandit is a simple but powerful algorithmic framework, and many effective algorithms have been proposed for various online models. In numerous applications, the decision-maker faces diminishing marginal utility. With non-linear aggregations, those algorithms often have poor regret bounds. Motivated by this, we study a bandit problem with diminishing marginal utility, which we termed the bandits with concave aggregated reward(BCAR). To tackle this problem, we propose two algorithms SW-BCAR and SWUCB-BCAR. Through theoretical analysis, we establish the effectiveness of these algorithms in addressing the BCAR issue. Extensive simulations demonstrate that our algorithms achieve better results than the most advanced bandit algorithms.
BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
Authors: Yonghao Yu; Shunan Zhu; Huai Qin; Haorui Li
Venue: IJCAI 2024 (main)

Abstract: Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slower pace. The synergistic integration of these methods holds substantial promise for advancing 3D generation techniques. In this paper, we present BoostDream, a highly efficient plug-and-play 3D refining method designed to transform coarse 3D assets into high-quality. The BoostDream framework comprises three distinct processes: (1) We introduce 3D model distillation that fits differentiable representations from the 3D assets obtained through feed-forward generation. (2) A novel multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion model to refine the 3D assets. (3) We propose to use prompt and multi-view consistent normal maps as guidance in refinement. Our extensive experiment is conducted on different differentiable 3D representations, revealing that BoostDream excels in generating high-quality 3D assets rapidly, overcoming the Janus problem compared to conventional SDS-based methods. This breakthrough signifies a substantial advancement in both the efficiency and quality of 3D generation processes.
FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise
Authors: Bixiao Zeng; Xiaodong Yang; Yiqiang Chen; Zhiqi Shen; Hanchao Yu; Yingwei Zhang
Venue: IJCAI 2024 (main)

Abstract: Federated learning (FL) facilitates collaborative model training across distributed clients while maintaining privacy. Federated noisy label learning (FNLL) is more of a challenge for data inaccessibility and noise heterogeneity. Existing works primarily assume clients are either noisy or clean, which may lack the flexibility to adapt to diverse label noise across different clients, especially when entirely clean or noisy clients are not the majority. To address this, we propose a general noise-robust federated learning framework called Federated Early-Stopping (FedES), which adaptively updates critical parameters of each local model based on their noise rates, thereby avoiding overfitting to noisy labels. FedES is composed of two stages: federated noise estimation and parameter-adaptive local updating \& global aggregation. We introduce a signed distance based on local and global gradients during a federated round to estimate clients' noise rates without requiring additional information. Based on this measure, we employ various degrees of early-stopping during local updating on the clients, and further, a noise-aware global aggregation is employed to achieve noise-robust learning. Extensive experiments conducted on varying synthetic and real-world label noise demonstrate the superior performance of FedES over the state-of-the-art methods.
Task-Agnostic Self-Distillation for Few-Shot Action Recognition
Authors: Bin Zhang; Yuanjie Dang; Peng Chen; Ronghua Liang; Nan Gao; Ruohong Huan; Xiaofei He
Venue: IJCAI 2024 (main)

Abstract: Task-oriented matching is one of the core aspects of few-shot Action Recognition. Most previous works leverage the metric features within the support and query sets of individual tasks, without considering the metric information across different matching tasks. This oversight represents a significant limitation in this task. Specifically, the task-specific metric feature can decrease the generalization ability and ignore the general matching feature applicable across different tasks. To address these challenges, we propose a novel meta-distillation framework for few-shot action recognition that learns the task-agnostic metric features and generalizes them to different tasks. First, to extract the task-agnostic metric information, we design a task-based self-distillation framework to learn the metric features from the training process progressively. Additionally, to enable the model with fine-grained matching capabilities, we design a multi-dimensional distillation module that extracts more detailed relations from the temporal, spatial, and channel dimensions within video pairs and improves the representative performance of metric features for each individual task. After that, the few-shot predictions can be obtained by feeding the embedded task-agnostic metric features to a common feature matcher. Extensive experimental results on standard datasets demonstrate our method’s superior performance compared to existing state-of-the-art methods.
Continual Multi-View Clustering with Consistent Anchor Guidance
Authors: Chao Zhang; Deng Xu; Xiuyi Jia; Chunlin Chen; Huaxiong Li
Venue: IJCAI 2024 (main)

Abstract: Multi-view clustering (MVC) has recently attracted much attention. Most existing approaches are designed for fixed multi-view data, and cannot deal with the common streaming data in real world. In this paper, we address this problem by proposing a consistent Anchor guided Continual MVC (ACMVC) method in a two-stage way. In initial learning stage, a low-rank anchor graph based model is constructed. In continual learning stage, to leverage the historical knowledge, the multi-level anchor information is reused to refine the model via adding consistency regularization. It not only provides prior knowledge to enhance the exploration on current data, but also captures the similarity relationship between previous and current data, enabling a comprehensive exploitation on streaming data. The proposed model can be optimized efficiently with linear time and space complexity. Experiments demonstrate the effectiveness and efficiency of our method compared with some state-of-the-art approaches.
Efficient Multi-view Unsupervised Feature Selection with Adaptive Structure Learning and Inference
Authors: Chenglong Zhang; Yang Fang; Xinyan Liang; Han Zhang; Peng Zhou; Xingyu Wu; Jie Yang; Bingbing Jiang; Weiguo Sheng
Venue: IJCAI 2024 (main)

Abstract: As data with diverse representations become high-dimensional, multi-view unsupervised feature selection has been an important learning paradigm. Generally, existing methods encounter the following challenges: (i) traditional solutions either concatenate different views or introduce extra parameters to weight them, affecting the performance and applicability; (ii) emphasis is typically placed on graph construction, yet disregarding the clustering information of data; (iii) exploring the similarity structure of all samples from the original features is suboptimal and extremely time-consuming. To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. Specifically, a parameter-free manner is devised to collaboratively fuse the membership matrices and graphs to learn the compatible structure information across all views, naturally balancing different views. Moreover, EMUFS leverages the similarity relations of data in the feature subspace induced by l2,0-norm to dynamically update the graph. Accordingly, the cluster information of anchors can be accurately propagated to samples via the graph structure and further guide feature selection, enhancing the quality of selected features and the computational costs in solution processes. A convergent optimization is developed to solve the formulated problem, and experiments demonstrate the effectiveness and efficiency of EMUFS.
Federated Adaptation for Foundation Model-based Recommendations
Authors: Chunxu Zhang; Guodong Long; Hongkuan Guo; Xiao Fang; Yang Song; Zhaojie Liu; Guorui Zhou; Zijian Zhang; Yang Liu; Bo Yang
Venue: IJCAI 2024 (main)

Abstract: With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users' private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user's personal preferences. Experimental results on four benchmark datasets demonstrate our method's superior performance. The code is available.
LSPAN: Spectrally Localized Augmentation for Graph Consistency Learning
Authors: Heng-Kai Zhang; Yi-Ge Zhang; Zhi Zhou; Yu-Feng Li
Venue: IJCAI 2024 (main)

Abstract: Graph-based consistency principle has been successfully applied to many semi-supervised problems in machine learning. Its performance largely depends on the quality of augmented graphs, which has been recently proven that revealing graph properties and maintaining the invariance of graphs are crucial for good performance. However, existing topology- or feature-based augmentation methods are spectrally non-localized -- important spectrums are disturbed throughout the entire frequency range, and their invariance may not be well preserved. Efforts on this issue remain to be limited. This paper proposes a simple yet effective model called Localized SPectral AugmentatioN (LSPAN), which perturbs a concentrated part of graph spectrum with equivalent intensity using Fourier orthogonality, so as to enhance graph spectrum preservation as well as model prediction. Moreover, it also avoids the significant training time of inverse Fourier transform. Extensive empirical evaluation on real-world datasets clearly shows the performance gain of spectrally localized augmentation, as well as its good convergence and efficiency compared to existing graph methods.
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Authors: Lefan Zhang; Zhang-Hao Tian; Wujun Zhou; Wei Wang
Venue: IJCAI 2024 (main)

Abstract: The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Existing methods are usually dependent on label frequency to tackle class imbalance, while the model bias on different classes is not directly related to label frequency and the true label frequency is inaccessible under label noise. To solve this, we propose a robust method for learning from long-tailed noisy data with sample selection and balanced loss. Specifically, we separate the noisy training data into clean labeled set and unlabeled set with sample selection, and train the deep neural network in a semi-supervised manner with a balanced loss based on model bias. Extensive experiments on benchmarks demonstrate that our method outperforms existing state-of-the-art methods.
CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection
Authors: Qin Zhang; Jiexin Lu; Xiaowei Li; Huisi Wu; Shirui Pan; Junyang Chen
Venue: IJCAI 2024 (main)

Abstract: As a popular task in graph learning, node classification seeks to assign labels to nodes, taking into account both their features and connections. However, an important challenge for its application in real-world scenarios is the presence of newly-emerged out-of-distribution samples and noisy samples, which affect the quality and robustness of learned classifiers. Out-of-distribution (OOD) samples are often found in both the training and testing phases. Such samples don’t belong to any known categories. These OOD samples are considered as outliers (OOD noise) when they appear during training, and are recognized as open-set samples during the testing. Meanwhile, in-distribution (IND) noisy data, i.e., known class samples with wrong labels, are also prevalent and inevitably degrade a model’s performance. The challenge of open-set learning with complex IND and OOD noise remains largely unexplored, particularly when dealing with non-IID graph data. To address these challenges, this paper introduces a novel complex-noise-resistant open-set node classification approach, designed for open-set graph data containing both IND and OOD noisy nodes. Specifically, a trustworthiness learner is adopted to learn the trustworthiness rates of the feature and label for each node while a decoder and an open-set classifier are trained to reconstruct the structure of a node and to predict its category simultaneously with the guidance of node trustworthiness. The experimental results demonstrate the superiority of our method.