Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, are usually designed only for a particular process in the AI development pipeline and do not efficiently use the inherent capacity of the LLMs. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML, i.e., from data retrieval to model deployment. AutoML-Agent takes user’s task descriptions, facilitates collaboration between specialized LLM agents, and delivers deployment-ready models. Unlike existing work, instead of devising a single plan, we introduce a retrieval-augmented planning strategy to enhance exploration to search for more optimal plans. We also decompose each plan into sub-tasks (e.g., data preprocessing and neural network design) each of which is solved by a specialized agent we build via prompting executing in parallel, making the search process more efficient. Moreover, we propose a multi-stage verification to verify executed results and guide the code generation LLM in implementing successful solutions. Extensive experiments on seven downstream tasks using fourteen datasets show that AutoML-Agent achieves a higher success rate in automating the full AutoML process, yielding systems with good performance throughout the diverse domains.

Abstract Point-of-interest (POI) recommendation systems play an important role in various location-based services by improving the user experience. Previous research has leveraged large-scale visit records to predict a user’s next visit POI based on the behavior of similar users. However, with the increasing emphasis on privacy preservation, there is a shift towards zero-shot recommendation that does not require training and only uses individual visit history data. As a better alternative to traditional zero-shot recommender systems, this paper proposes a novel zero-shot recommender system leveraging the ability of pre-trained large language models (LLMs) to understand human behavior called ZeroPOIRec . ZeroPOIRec involves a profiler module that enables LLMs to extract individual user preferences from multiple aspects, including spatio-temporal patterns and individual characteristics, and a recommender module that enhances the zero-shot POI recommendation performance via candidate refinement and prioritization. Through experiments using a benchmark dataset and a newly introduced real-world dataset with semantic variables, we demonstrate that, despite ZeroPOIRec being a zero-shot approach, it outperforms state-of-the-art methods in terms of recommendation performance.

This paper presents AnoViz, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. AnoViz provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.

Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk to reduce future accidents. In this paper, we use dangerous driving statistics from driving log data and multi-graph learning to enhance predictive performance. We first conduct geographical and temporal correlation analyses to quantify the relationship between dangerous driving and actual accidents. Then, to learn various dependencies between districts besides the traditional adjacency matrix, we simultaneously model both static and dynamic graphs representing the spatio-temporal contextual relationships with heterogeneous environmental data, including the dangerous driving behavior. A graph is generated for each type of the relationships. Ultimately, we propose an end-to-end framework, called MG-TAR, to effectively learn the association of multiple graphs for accident risk prediction by adopting multi-view graph neural networks with a multi-attention module. Thorough experiments on ten real-world datasets show that, compared with state-of-the-art methods, MG-TAR reduces the error of predicting the accident risk by up to 23% and improves the accuracy of predicting the most dangerous areas by up to 27%.

Because traffic accidents cause huge social and economic losses, it is of prime importance to precisely predict the traffic accident risk for reducing future accidents. In this paper, we propose a Deep Fusion network for citywide Traffic Accident Risk prediction (DF-TAR) with dangerous driving statistics that contain the frequencies of various dangerous driving offences in each region. Our unique contribution is to exploit these statistics, obtained by processing the data from in-vehicle sensors, for modeling the traffic accident risk. Toward this goal, we first examine the correlation between dangerous driving offences and traffic accidents, and the analysis shows a strong correlation between them in terms of both location and time. Specifically, quick start (0.83), rapid acceleration (0.76), and sharp turn (0.76) are the top three offences that have the highest average correlation scores. We then train the DF-TAR model using the dangerous driving statistics as well as external environmental features. By extensive experiments on various frameworks, the DF-TAR model is shown to improve the accuracy of the baseline models by up to 54% by virtue of the integration of dangerous driving into the modeling of traffic accident risk.

Complex event processing (CEP) is defined as event processing for multiple stream sources to infer events that suggest complicated circumstances. As the size of stream data becomes larger, CEP engines have been parallelized to take advantage of distributed computing. Typically, deployment of such a distributed CEP engine involves manual configuration, which has been regarded as an obstacle to its widespread adoption. In this demonstration, we present CEP-Wizard, a framework of automatically configuring and deploying a distributed CEP engine with minimum effort. The demonstration shows that even inexperienced users can easily configure and deploy it on Apache Storm with achieving high performance and low resource usage.