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In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in the existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. Instead of manually tuning optimal anchor with massive hyper-parameters, we propose to optimize the contribution weights of a group of pre-defined anchor numbers to avoid extra time expenditure among views. Most importantly, we propose a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion. Then, an efficient linear optimization algorithm is proposed to solve the resultant problem. Comprehensive experimental results demonstrate the effectiveness and efficiency of our proposed framework.

AAAI 2023

Let the data choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering

graph-based machine learning

multi-view learning

clustering

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulating a new variant of MAPP called periodic MAPP in which the timing of agent appearances is periodic. The objective with periodic MAPP is to find a periodic plan, a set of collision-free trajectories that the agent streams can use repeatedly over periods, with periods that are as small as possible. To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. We show that the periodic plans once found can be used for a more practical case in which agents in a stream can appear at random times. We confirm the effectiveness of our method compared with baseline methods in terms of throughput in several scenarios that abstract autonomous intersection management tasks.

Periodic Multi-Agent Path Planning

In recent years, skeleton-based action recognition has achieved remarkable performance in understanding human motion from sequences of skeleton data, which is an important medium for synthesizing realistic human movement in various applications. However, existing methods assume that each action clip is manually trimmed to contain one specific action, which requires a significant amount of effort for annotation. To solve this problem, we consider a novel problem of skeleton-based weakly-supervised temporal action localization (S-WTAL), where we need to recognize and localize human action segments in untrimmed skeleton videos given only the video-level labels. Although this task is challenging due to the sparsity of skeleton data and the lack of contextual clues from interaction with other objects and the environment, we present a frame-level label refinement framework based on a spatio-temporal graph convolutional network (ST-GCN) to overcome these difficulties. We use multiple instance learning (MIL) with video-level labels to generate the frame-level predictions. Inspired by advances in handling the noisy label problem, we introduce a label cleaning strategy of the frame-level pseudo labels to guide the learning process. The network parameters and the frame-level predictions are alternately updated to obtain the final results. We extensively evaluate the effectiveness of our learning approach on skeleton-based action recognition benchmarks. The state-of-the-art experimental results demonstrate that the proposed method can recognize and localize action segments of the skeleton data.

Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition

Data imbalance, also known as the long-tailed distribution of data, is an important challenge for data-driven models. In the Word Sense Disambiguation (WSD) task, the long-tail phenomenon of word sense distribution is more significant, making it difficult to effectively represent and identify Long-Tail Senses (LTSs). Therefore exploring representation methods that do not rely heavily on the training sample size is an important way to combat LTSs. Considering that many new states, that is, superposition states, can be constructed from several known states in quantum mechanics, the concept of superposition states provides the possibility to obtain more accurate representations from inferior representations learned from a small sample size. In this article, inspired by quantum superposition states, a representation method in Hilbert space is proposed to reduce the dependence on large sample sizes and thus combat LTSs. We theoretically prove the correctness of the method, and verify its effectiveness under the standard WSD evaluation framework and obtain state-of-the-art performance. Furthermore, we also test on the constructed LTS and latest cross-lingual datasets, and achieve promising results.

Quantum-inspired Representation for Long-tail Senses of Word Sense Disambiguation

It is an open question what semantic representations transformer-based language models can encode and whether they have access to more abstract aspects of semantic meaning. Here, we propose a diagnostic dataset to investigate how well language models understand the degree semantics of adjectives. In the dataset, referred as the Adjective Scale Probe (ASP), we semi-automatically generate 8 tests of Natural Language Inference (NLI) questions to test 8 key capabilities of adjective interpretation. We apply the ASP dataset to evaluate the performance of 3 language models, i.e., BERT, DeBERTa, and T0. It is found that language models perform below the majority baseline for most tests of the ASP, even when the models have been fine-tuned to achieve high performance on the large-scale MNLI dataset. But after we fine-tune the pre-trained models on a subset of the ASP, DeBERTa can achieve high performance on the untrained adjectives and untrained tests, suggesting that DeBERTa may have captured degree semantic information of adjectives through pre-training but it needs specific training data to learn how to apply such information to the current tasks. In sum, the ASP provides an easy-to-use method to test fine-grained formal semantic properties of adjectives, and reveals language models' abilities to access formal semantic information.

Adjective Scale Probe: Can Language Models Encode Formal Semantics Information?

Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters.
This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other's data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution.
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces

Stochastic gradient descent  algorithm  (SGD)  has been popular in various fields of artificial intelligence as well as a prototype of online learning algorithms. This article proposes a novel and general framework of one-sided testing for streaming data based on SGD, which  determines whether the unknown parameter is greater than a certain positive constant. We construct the online-updated test statistic sequentially by integrating the selected batch-specific estimator or its opposite, which is referred to opposite online learning. The batch-specific online estimators are chosen strategically according to the proposed sequential tactics designed by two-armed bandit process. Theoretical results prove the advantage of the strategy ensuring the distribution of test statistic to be optimal under the null hypothesis and also supply the theoretical evidence of power enhancement compared with classical test statistic. In application, the proposed method is appealing for statistical inference of one-sided testing because it is scalable for any model. Finally, the superior finite-sample performance is evaluated by simulation studies.

Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators

Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg. 

Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint

Difference-of-Convex (DC) minimization, referring to the problem of minimizing the difference of two convex functions, has been found rich applications in statistical learning and studied extensively for decades. However, existing methods are primarily based on multi-stage convex relaxation, only leading to weak optimality of critical points. This paper proposes a coordinate descent method for minimizing a class of DC functions based on sequential nonconvex approximation. Our approach iteratively solves a nonconvex one-dimensional subproblem globally, and it is guaranteed to converge to a coordinate-wise stationary point. We prove that this new optimality condition is always stronger than the standard critical point condition and directional point condition under a mild \textit{locally bounded nonconvexity assumption}. For comparisons, we also include a naive variant of coordinate descent methods based on sequential convex approximation in our study. When the objective function satisfies a \textit{globally bounded nonconvexity assumption} and \textit{Luo-Tseng error bound assumption}, coordinate descent methods achieve \textit{Q-linear} convergence rate. Also, for many applications of interest, we show that the nonconvex one-dimensional subproblem can be computed exactly and efficiently using a breakpoint searching method. Finally, we have conducted extensive experiments on several statistical learning tasks to show the superiority of our approach.

Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Deep neural networks (DNNs) have witnessed remarkable achievement in image super-resolution (SR), and plenty of DNN-based SR models with elaborated network designs have recently been proposed. However, existing methods usually require substantial computations by operating in spatial domain. To address this issue, we propose a general frequency-oriented framework (FSR) to accelerate SR networks by considering data characteristics in frequency domain. Our FSR mainly contains dual feature aggregation module (DFAM) to extract informative features in both spatial and transform domains, followed by a four-path SR-Module with different capacities to super-resolve in the frequency domain. Specifically, DFAM further consists of a transform attention block (TABlock) and a spatial context block (SCBlock) to extract global spectral information and local spatial information, respectively, while SR-Module is a parallel network container that contains four to-be-accelerated branches. Furthermore, we propose an adaptive weight strategy for a trade-off between image details recovery and visual quality. Extensive experiments show that our FSR can save FLOPs by almost 40% while reducing inference time by 50% for other SR methods (e.g., FSRCNN, CARN, SRResNet and RCAN).

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