Extreme Algorithm Selection With Dyadic Feature Representation
Heinz Nixdorf Institute（海因茨·尼克斯多夫研究所）；Department of Computer Science, Paderborn University（帕德博恩大学计算机科学系）；Paderborn University（帕德博恩大学）；Heinz Nixdorf Institute and Department of Computer Science, Paderborn University（海因茨·尼克斯多夫研究所和帕德博恩大学计算机科学系）
Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in combined algorithm selection and hyperparameter optimization problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. Therefore, here we propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms, facilitating meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described. We find the latter to improve significantly over the current state of the art in various metrics.
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
University of Turku（图尔库大学）；Turku Centre for Computer Science（图尔库计算机科学中心）；Ghent University（根特大学）；Institute for Molecular Medicine Finland (FIMM)（芬兰分子医学研究所（FIMM））
Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning
Environmental Studies Department and the Environmental Markets Labs (emLab) of the University of California Santa Barbara（加州大学圣芭芭拉分校环境研究系及环境市场实验室）；Applied Probability and Statistics Department of the University of California Santa Barbara（加州大学圣芭芭拉分校应用概率与统计系）
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
Thales Research & Technology（泰雷兹研究与技术公司）；École Polytechnique（巴黎综合理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）
