The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available https://github.com/Zhangcx19/GPFedRec

Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first research work in federated recommendation to specifically study the cold-start scenario. The proposed method learns two sets of item representations by leveraging item attributes and interaction records simultaneously. Additionally, an item representation alignment mechanism is designed to align two item representations and learn the meta attribute network at the server within a federated learning framework. Experiments on four benchmark datasets demonstrate IFedRec's superior performance for cold-start scenarios. Furthermore, we also verify IFedRec owns good robustness when the system faces limited client participation and noise injection, which brings promising practical application potential in privacy-protection enhanced federated recommendation systems. The implementation code is available

We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problems, the MCFS problem commonly occurs in practical applications but has been rarely studied in previous literature. It brings new challenges of distinguishing between many classes given only a few training samples per class. In this article, we leverage the class hierarchy as a prior knowledge to train a coarse-to-fine classifier that can produce accurate predictions for MCFS problem in both settings. The propose model, “memory-augmented hierarchical-classification network (MahiNet)”, performs coarse-to-fine classification where each coarse class can cover multiple fine classes. Since it is challenging to directly distinguish a variety of fine classes given few-shot data per class, MahiNet starts from learning a classifier over coarse-classes with more training data whose labels are much cheaper to obtain. The coarse classifier reduces the searching range over the fine classes and thus alleviates the challenges from “many classes”. On architecture, MahiNet first deploys a convolutional neural network (CNN) to extract features. It then integrates a memory-augmented attention module and a multi-layer perceptron (MLP) together to produce the probabilities over coarse and fine classes. While the MLP extends the linear classifier, the attention module extends the KNN classifier, both together targeting the “few-shot” problem. We design several training strategies of MahiNet for supervised learning and meta-learning. In addition, we propose two novel benchmark datasets “mcfsImageNet” (as a subset of ImageNet) and “mcfsOmniglot” (re-splitted Omniglot) specially designed for MCFS problem. In experiments, we show that MahiNet outperforms several state-of-the-art models (e.g., prototypical networks and relation networks) on MCFS problems in both supervised learning and meta-learning.

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them (a.k.a. knowledge graph completion). Prevalent graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements (i.e., entities/relations) into dense embeddings and capturing their triple-level relationship with spatial distance. However, they are hardly generalizable to the elements never visited in training and are intrinsically vulnerable to graph incompleteness. In contrast, textual encoding approaches, e.g., KG-BERT, resort to graph triple’s text and triple-level contextualized representations. They are generalizable enough and robust to the incompleteness, especially when coupled with pre-trained encoders. But two major drawbacks limit the performance: (1) high overheads due to the costly scoring of all possible triples in inference, and (2) a lack of structured knowledge in the textual encoder. In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques – a complementary hybrid of both paradigms. Specifically, we partition each triple into two asymmetric parts as in translation-based graph embedding approach, and encode both parts into contextualized representations by a Siamese-style textual encoder. Built upon the representations, our model employs both deterministic classifier and spatial measurement for representation and structure learning respectively. It thus reduces the overheads by reusing graph elements’ embeddings to avoid combinatorial explosion, and enhances structured knowledge by exploring the spatial characteristics. Moreover, we develop a self-adaptive ensemble scheme to further improve the performance by incorporating triple scores from an existing graph embedding model. In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a sophisticated textual encoding method.

This paper proposes multi-task copula (MTC) that can handle a much wider class of tasks than mean regression with Gaussian noise in most former multi-task learning (MTL). While former MTL emphasizes shared structure among models, MTC aims at joint prediction to exploit inter-output correlation. Given input, the outputs of MTC are allowed to follow arbitrary joint continuous distribution. MTC captures the joint likelihood of multi-output by learning the marginal of each output firstly and then a sparse and smooth output dependency graph function. While the former can be achieved by classical MTL, learning graphs dynamically varying with input is quite a challenge. We address this issue by developing sparse graph regression (SpaGraphR), a non-parametric estimator incorporating kernel smoothing, maximum likelihood, and sparse graph structure to gain fast learning algorithm. It starts from a few seed graphs on a few input points, and then updates the graphs on other input points by a fast operator via coarse-to-fine propagation. Due to the power of copula in modeling semi-parametric distributions, SpaGraphR can model a rich class of dynamic non-Gaussian correlations. We show that MTC can address more flexible and difficult tasks that do not fit the assumptions of former MTL nicely, and can fully exploit their relatedness. Experiments on robotic control and stock price prediction justify its appealing performance in challenging MTL problems.

Nonnegative matrix factorization (NMF) becomes tractable in polynomial time with unique solution under separability assumption, which postulates all the data points are contained in the conical hull of a few anchor data points. Recently developed linear programming and greedy pursuit methods can pick out the anchors from noisy data and results in a near-separable NMF. But their efficiency could be seriously weakened in high dimensions. In this paper, we show that the anchors can be precisely located from low-dimensional geometry of the data points even when their high dimensional features suffer from serious incompleteness. Our framework, entitled divide-and-conquer anchoring (DCA), divides the high-dimensional anchoring problem into a few cheaper sub-problems seeking anchors of data projections in low-dimensional random spaces, which can be solved in parallel by any near-separable NMF, and combines all the detected low-dimensional anchors via a fast hypothesis testing to identify the original anchors. We further develop two non-iterative anchoring algorithms in 1D and 2D spaces for data in convex hull and conical hull, respectively. These two rapid algorithms in the ultra low dimensions suffice to generate a robust and efficient near-separable NMF for high-dimensional or incomplete data via DCA. Compared to existing methods, two vital advantages of DCA are its scalability for big data, and capability of handling incomplete and high-dimensional noisy data. A rigorous analysis proves that DCA is able to find the correct anchors of a rank-k matrix by solving math cal O(klog k) sub-problems. Finally, we show DCA outperforms state-of-the-art methods on various datasets and tasks.

The least squares problem is one of the most important regression problems in statistics, machine learning and data mining. In this paper, we present the Constrained Stochastic Gradient Descent (CSGD) algorithm to solve the large-scale least squares problem. CSGD improves the Stochastic Gradient Descent (SGD) by imposing a provable constraint that the linear regression line passes through the mean point of all the data points. It results in the best regret bound $O(\log{T})$, and fastest convergence speed among all first order approaches. Empirical studies justify the effectiveness of CSGD by comparing it with SGD and other state-of-the-art approaches. An example is also given to show how to use CSGD to optimize SGD based least squares problems to achieve a better performance.

In multimedia retrieval, multi-label annotation for image, text and video is challenging and attracts rapidly growing interests in past decades. The main crux of multi-label annotation lies on 1) how to reduce the model complexity when the label space expands exponentially with the increase of the number of labels; and 2) how to leverage the label correlations which have broadly believed useful for boosting annotation performance. In this paper, we propose "labelsets anchored subspace ensemble (LASE)" to solve both problems in an efficient scheme, whose training is a regularized matrix decomposition and prediction is an inference of group sparse representations. In order to shrink the label space, we firstly introduce "label distilling" extracting the frequent labelsets to replace the original labels. In the training stage, the data matrix is decomposed as the sum of several low-rank matrices and a sparse residual via a randomized optimization, where each low-rank part defines a feature subspace mapped by a labelset. A manifold regularization is applied to map the labelset geometry to the geometry of the obtained subspaces. In the prediction stage, the group sparse representation of a new sample on the subspace ensemble is estimated by group lasso. The selected subspaces indicate the labelsets that the sample should be annotated with. Experiments on several benchmark datasets of texts, images, web data and videos validate the appealing performance of LASE in multi-label annotation.

Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models, e.g., classical SVM, linear programming SVM and least square SVM. Compared against SVM-Perf (whose convergence rate in solving the dual SVM is upper bounded by O(1/√k) where k is the number of iterations) and Pegasos (online SVM that converges at rate O(1/k) for the primal SVM), NESVM achieves the optimal convergence rate at O(1/k2) and a linear time complexity. In particular, NESVM smoothes the nondifferentiable hinge loss and ℓ1-norm in the primal SVM. Then the optimal gradient method without any line search is adopted to solve the optimization. In each iteration round, the current gradient and historical gradients are combined to determine the descent direction, while the Lipschitz constant determines the step size. Only two matrix-vector multiplications are required in each iteration round. Therefore, NESVM is more efficient than existing SVM solvers. In addition, NESVM is available for both linear and nonlinear kernels. We also propose "homotopy NESVM" to accelerate NESVM by dynamically decreasing the smooth parameter and using the continuation method. Our experiments on census income categorization, indoor/outdoor scene classification event recognition and scene recognition suggest the efficiency and the effectiveness of NESVM. The MATLAB code of NESVM will be available on our website for further assessment.