The existing Click-Through Rate (CTR) algorithms have their own advantages and are sensitive to hyperparameters. Quickly obtaining a high-performance CTR model for the new task can bring good application effects. However, ordinary users fail to do so due to the lack of domain knowledge. In this paper, we remedy this deficiency by proposing AutoCTR, an efficient meta-learning based Combined Algorithm Selection and Hyperparameter Optimization (CASH) algorithm, to help non-expert users quickly find the best CTR model. In AutoCTR, we introduce the meta-learning technique to make full use of the meta-information w.r.t. CTR to guide for the new CTR task. Specifically, we utilize the meta-information to learn characteristics and representations of CTR algorithms with different settings. We use these meta experiences combined with few evaluation information on the target CTR dataset to efficiently exploring the huge CTR CASH search space for the new task. The CTR model representation method has significant influence on the quality of the learned meta experiences. To further enhance the experiences quality, we also design a Graph Neural Network (GNN) based embedding learning method. This method can link different CTR models through their components, and thus quickly learning higher-quality model representations. Extensive experimental results show that AutoCTR can quickly select suitable CTR models for different CTR tasks. Compared with the existing CASH algorithms, which ignore meta-information or rely on a huge amount of meta-information, AutoCTR is more reasonable and efficient.

Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments. Despite their great success, the selection of suitable compression methods and design of details of the compression scheme are difficult, requiring lots of domain knowledge as support, which is not friendly to non-expert users. To make more users easily access to the model compression scheme that best meet their needs, in this paper, we propose AutoMC, an effective and efficient automatic tool for model compression. In order to improve the search efficiency and quality, in AutoMC, we build the domain knowledge on model compression to deeply understand the characteristics and advantages of each compression method under different settings. This method can provide AutoMC with the more reasonable guidance and thus reduce useless evaluation. In addition, we present a progressive search strategy to efficiently explore pareto optimal compression scheme according to the learned prior knowledge combined with the historical evaluation information. This strategy can help AutoMC selectively and gradually explore more valuable search space, and thus reduce the search difficulty and improve the search efficiency. Extensive experimental results show that AutoMC can provide users with better compression schemes within short time compared to the existing compression methods and AutoML algorithms, which demonstrates the effectiveness and significance of our proposed algorithm.

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients’ data. Recently, many strategies have been proposed to improve the generality of the global model and thus improve FL effect. However, existing strategies either ignore the fairness among clients or sacrifice performance for fairness. They cannot ensure that the gap among clients is as small as possible without sacrificing federated performance. To address this issue, we propose ParetoFed , a new local information aggregation method dedicated to obtaining better federated performance with smaller gap among clients. Specifically, we propose to use multi-objective hyperparameter optimization (HPO) algorithm to gain global models that are both fair and effective. Then, we send Pareto Optimal global models to each client, allowing them to choose the most suitable one as the base to optimize their local model. ParetoFed not only make the global models more fair but also make the selection of local models more personalized, which can further improve the federated performance. Extensive experiments show that ParetoFed outperforms existing FL methods in terms of fairness, and even achieves better federated performance, which demonstrates the significance of our method.

SequentialRecommendation (SR) System emerged recently as a powerful tool for suggesting users with the next item of interest. Despite their great success, the design of SR systems requires heavy manual work and domain knowledge. In this paper, we present$\mathbf {AutoSR}$, an effectiveAutomatedMachineLearning (AutoML) tool that enables automatic design of powerful SR systems based onGraphNeuralNetwork (GNN) andReinforcementLearning (RL). In$\mathbf {AutoSR}$, we summarize the design process of the SR systems and extract effective operations from the existing SR systems to construct our search space. Such an experience-based search space generates diverse SR systems by integrating effective operations of different systems, providing a basic condition for the implementation of AutoML. Besides, we propose a graph-based RL method to efficiently explore the SR search space, where operations have complex and diverse application conditions. Compared with the existing AutoML methods, which ignore potential relations among operations,$\mathbf {AutoSR}$can greatly avoid invalid SR system design and efficiently discover more powerful SR systems by analyzing the relation graph of various operations. Extensive experimental results show that$\mathbf {AutoSR}$can gain powerful SR systems, superior to the existing$\mathbf {AutoSR}$systems used for search space construction. Besides,$\mathbf {AutoSR}$is more efficient than the existing AutoML algorithms in SR system design, which demonstrate the superiority of$\mathbf {AutoSR}$.

With years of development, a significant number of Time Series Classification (TSC) algorithms have been proposed and applied to various fields such as scientific research and industry scenarios, including traditional statistical methods, machine learning methods, and recently deep learning models. However, choosing a suitable model along with good parameter values that perform well on a given task, which is also known as Combined Algorithm Selection and Hyperparameter optimization problem (CASH), is still challenging. How to automatically select the appropriate algorithm according to the task during analyzing is a topic worthy of further research. Nevertheless, for TSC, a field that has been developed for decades, there is no effective and efficient approach for automatic algorithm selection. To the best of our knowledge, the current approach is based on genetic search, which is very computationally intensive and time-consuming. Therefore, in this paper, we propose TSC-AutoML, a zero-configuration and meta-learning-based approach for the automatic Time Series Classification algorithm CASH (also known as TSC-CASH). TSC-AutoML extracts knowledge from historical tasks and performs automatic feature selection and knowledge filtering with a reinforcement learning policy. The experience extracted is filtered and transformed into metadata. The meta-learner trained on the metadata together with our proposed warm start strategy will select an optimal algorithm for tasks uploaded by users, and then our proposed Hyperparameter Optimization method based on the Fast Warm Start strategy searches for hyperparameter combinations of the selected algorithm and adjusts parameter configuration to achieve top performance. The entire process is pre-trained, automated for the new task, and parameter-free for the user to decide, making it easy for users with the little domain experience to get started easily. Experimental results illustrate that TSC-AutoML outperforms existing methods in terms of both time and accuracy of optimum algorithm selection.

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this article, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.

GraphNeuralNetwork (GNN) has achieved great success in the field of graph data processing and analysis, but the design of GNN architecture is difficult and time-consuming. To reduce the development cost of GNNs, recently, someGNNNeuralArchitectureSearch (GNN NAS) techniques are presented for the automatic design of GNN architectures. These techniques bring great convenience to the use of GNN, but cannot be applied to the federated learning scenarios. They only consider the single-source graph dataset, while failing to deal with the distributed and private graph datasets, which limits their applications. To address this shortcoming, in this paper we propose FL-AGNNS, an efficient GNN NAS algorithm which enables distributed agents to cooperatively design powerful GNN models while keeping personal information on local devices. FL-AGNNS designs a novel federated evolutionary optimization strategy. This strategy can fully consider the GNN architectures favored by each client, thus recommend GNN architectures that perform well in multiple datasets. In additions, FL-AGNNS applies the GNN super-network, a weight sharing strategy, to speed up the evaluation of GNN models during the search phase. Extensive experimental results show that FL-AGNNS can recommend better GNN models in short time under the federated learning framework, surpassing the state-of-the-arts GNN models.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time.

A reasonable and effective community scoring function is of great significance since it can measure the community quality of groups we found more properly and help us discover more valuable communities. In this paper, we propose a new community scoring function, ECOQUG. Different from the existing community scoring functions, ECOQUG is designed based on the experimental study and theoretical analysis of groups with different community qualities. ECOQUG is more convincing. In addition, we design a series of experiments to examine the effectiveness and accuracy of ECOQUG and 13 other classic community scoring functions comprehensively. The extensive experimental results show that ECOQUG is effective and better than other community scoring functions.