Learning an appropriate (dis)similarity function from the available data is a central problem in machine learning, since the success of many machine learning algorithms critically depends on the choice of a similarity function to compare examples. Despite many approaches for similarity metric learning have been proposed, there is little theoretical study on the links between similarity met- ric learning and the classification performance of the result classifier. In this paper, we propose a regularized similarity learning formulation associated with general matrix-norms, and establish their generalization bounds. We show that the generalization error of the resulting linear separator can be bounded by the derived generalization bound of similarity learning. This shows that a good gen- eralization of the learnt similarity function guarantees a good classification of the resulting linear classifier. Our results extend and improve those obtained by Bellet at al. [3]. Due to the techniques dependent on the notion of uniform stability [6], the bound obtained there holds true only for the Frobenius matrix- norm regularization. Our techniques using the Rademacher complexity [5] and its related Khinchin-type inequality enable us to establish bounds for regularized similarity learning formulations associated with general matrix-norms including sparse L 1 -norm and mixed (2,1)-norm.
Tailed Low-Rank Matrix Factorization for Similarity Matrix Completion
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人中心）；The Hong Kong Polytechnic University（香港理工大学）
Southern University of Science and Technology（南方科技大学）；Sun Yat-sen University（中山大学）
Tensor decomposition is an effective tool for learning multi-way structures and heterogeneous features from high-dimensional data, such as the multi-view images and multichannel electroencephalography (EEG) signals, are often represented by tensors. However, most of tensor decomposition methods are the linear feature extraction techniques, which are unable to reveal the nonlinear structure within high-dimensional data. To address such problem, a lot of algorithms have been proposed for simultaneously performs linear and non-linear feature extraction. A representative algorithm is the Graph Reg…
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Beijing Institute of Technology（北京理工大学）；Hong Kong Baptist University（香港浸会大学）
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-netw…
Graph Similarity Regularized Softmax for Semi-Supervised Node Classification
School of Mathematics and Physics, North China Electric Power University（华北电力大学数理学院）；School of Mathematical Sciences, Beijing Normal University（北京师范大学数学科学学院）
hSNMF: Hybrid Spatially Regularized NMF for Image-Derived Spatial Transcriptomics
Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）；Department of Genomic Medicine and MDACC Epigenomics Therapy Initiative (METI)（基因医学系和MDACC表观遗传学治疗计划（METI））；MD Anderson Cancer Center, Houston TX, USA（MD安德森癌症中心，休斯顿德克萨斯州，美国）；Department of Information Science Technology, University of Houston（信息科学与技术系，休斯顿大学）
Implicitly Normalized Online PCA: A Regularized Algorithm with Exact High-Dimensional Dynamics
Machine Learning and Information Processing Group, KUIS AI Center（机器学习与信息处理组，KUIS人工智能中心）；Department of Electrical and Electronics Engineering（电子与电气工程系）
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Department of Computing Science, University of Alberta, Canada（阿尔伯塔大学计算机科学系）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能 chair）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
The University of Sydney（悉尼大学）；Hong Kong Baptist University（香港浸会大学）；The University of Melbourne（墨尔本大学）；Xidian University（西安电子科技大学）；Brain-Inspired Technology Co., Ltd.（脑启发技术有限公司）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Semi-supervised Learning with Regularized Laplacian
Inria（法国国家信息与自动化研究所）；Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences（俄罗斯科学院特拉佩兹尼科夫控制科学研究所）；St. Petersburg State University（圣彼得堡国立大学）
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images
Lakehead University（湖首大学）；University of Calgary（卡尔加里大学）
Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We discover that BERT risks neglecting the contextual word similarity in pre-training. To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity. By predicting masked words and aligning contextual embeddings…
University of Central Florida（中佛罗里达大学）；State University of New York at Albany（纽约州立大学奥尔巴尼分校）
While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors. To address these issues, this paper proposes a framework of matrix variate regression models based on a rank constraint, vector regularization (e.g., sparsity), and a general loss function with three special cases considered: ordinary matrix regression, robust matrix regression, and matrix logistic regression. We also propose an altern…
The similarity between objects is significant in a broad range of areas. While similarity can be measured using off-the-shelf distance functions, they may fail to capture the inherent meaning of similarity, which tends to depend on the underlying data and task. Moreover, conventional distance functions limit the space of similarity measures to be symmetric and do not directly allow comparing objects from different spaces. We propose using quantum networks (GQSim) for learning task-dependent (a)symmetric similarity between data that need not have the same dimensionality. We analyze the properti…
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Southern University of Science and Technology（南方科技大学）；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）
Similarity-based Learning via Data Driven Embeddings
Microsoft Research India（微软印度研究院）
我们考虑使用相似性/距离函数的数据分类的问题。具体而言，我们提出了一个框架，用于定义一个（DIS）相似性函数相对于一个给定的学习任务的善良，并提出了算法，保证泛化性能时，这样的好功能。我们的框架统一和推广了[Balcan-Blum ICML 2006]和[Wang et al ICML 2007]提出的框架。我们的框架的一个有吸引力的特点是它的适应性数据-我们不提倡一个固定的概念的善良，而是让数据支配it. We显示，通过给予理论保证，最适合于一个问题的善良准则本身可以学习，这使得我们的方法适用于各种领域和问题。我们提出了一种基于地标的方法来获得一个分类器，从这样的学习良好的标准。然后，我们提供了一种新的多样性为基础的启发式执行任务驱动的选择的标志点，而不是随机选择。我们在各种基于相似性的学习数据集和基准UCI数据集上证明了我们的优度标准学习方法以及地标选择启发式的有效性，在这些数据集上，我们的方法始终优于现有方法。
KU Leuven（鲁汶大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）
This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nyström approximation for scalability in large sample cas…
Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
