AutoML @ NeurIPS 2018 challenge: Design and Results
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.
Code (0)
등록된 구현이 없습니다.
Tasks
AutoMLBIG-bench Machine LearningLifelong learningSimilar Papers 제목 키워드 기반
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift
Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows. To this end, Automated Machine Learning (AutoML) has eme…
AutoMLBIG-bench Machine LearningLifelong learningDREAM: Debugging and Repairing AutoML Pipelines
Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically sea…
AutoMLNeurIPS should lead scientific consensus on AI policy
Designing wise AI policy is a grand challenge for society. To design such policy, policymakers should place a premium on rigorous evidence and scientific consensus. While several mechanisms exist for evidence generation,…
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper, we introduce new Auto…
AutoMLBIG-bench Machine LearningMeta-LearningCan Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisio…
AutoMLFairness