{"total":18,"rows":[{"id":"2101.05273","title":"AutoDS: Towards Human-Centered Automation of Data Science","created":"2021-01-13","cited_by_count":32,"area":null},{"id":"2302.05927","title":"Transfer Learning for Bayesian Optimization: A Survey","created":"2023-02-12","cited_by_count":27,"area":null},{"id":"2107.08861","title":"VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition","created":"2021-07-19","cited_by_count":25,"area":null},{"id":"2101.03970","title":"How Much Automation Does a Data Scientist Want?","created":"2021-01-07","cited_by_count":20,"area":null},{"id":"2104.04375","title":"Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML","created":"2021-04-09","cited_by_count":12,"area":null},{"id":"2106.07714","title":"Learning Deep Morphological Networks with Neural Architecture Search","created":"2021-06-14","cited_by_count":6,"area":null},{"id":"2311.07870","title":"AutoML for Large Capacity Modeling of Meta's Ranking Systems","created":"2023-11-14","cited_by_count":4,"area":null},{"id":"2201.01289","title":"Self-directed Machine Learning","created":"2022-01-04","cited_by_count":3,"area":null},{"id":"2105.00282","title":"Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning","created":"2021-05-01","cited_by_count":2,"area":null},{"id":"2105.01840","title":"CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysis","created":"2021-05-05","cited_by_count":2,"area":null},{"id":"2106.07317","title":"Automated Machine Learning Techniques for Data Streams","created":"2021-06-14","cited_by_count":2,"area":null},{"id":"2110.13629","title":"Bayesian Optimization and Deep Learning forsteering wheel angle prediction","created":"2021-10-22","cited_by_count":2,"area":null},{"id":"2210.01792","title":"Sampling Streaming Data with Parallel Vector Quantization -- PVQ","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2102.11267","title":"Interpret-able feedback for AutoML systems","created":"2021-02-22","cited_by_count":0,"area":null},{"id":"2107.01253","title":"Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization","created":"2021-07-02","cited_by_count":0,"area":null},{"id":"2206.09423","title":"Efficient End-to-End AutoML via Scalable Search Space Decomposition","created":"2022-06-19","cited_by_count":0,"area":null},{"id":"2208.04376","title":"On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning","created":"2022-08-08","cited_by_count":0,"area":null},{"id":"2303.10732","title":"AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting","created":"2023-03-19","cited_by_count":0,"area":null}],"next_cursor":null}