[2603.23573] Dual-Criterion Curriculum Learning: Application to Temporal Data

Computer Science > Machine Learning arXiv:2603.23573 (cs) [Submitted on 24 Mar 2026] Title:Dual-Criterion Curriculum Learning: Application to Temporal Data Authors:Gaspard Abel, Eloi Campagne, Mohamed Benloughmari, Argyris Kalogeratos View a PDF of the paper titled Dual-Criterion Curriculum Learning: Application to Temporal Data, by Gaspard Abel and Eloi Campagne and Mohamed Benloughmari and Argyris Kalogeratos View PDF HTML (experimental) Abstract:Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and most usually the main bottleneck for effective learning, while also in many cases the employed heuristics are only application-specific. In this work, we propose the Dual-Criterion Curriculum Learning (DCCL) framework that combines two views of assessing instance-wise difficulty: a loss-based criterion is complemented by a density-based criterion learned in the data representation space. Essentially, DCCL calibrates training-based evidence (loss) under the consideration that data sparseness amplifies the learning difficulty. As a testbed, we choose the time-series forecasting task. We evaluate our framework on multivariate time-series benchmarks under standard One-Pass and Baby-Steps training schedules. Empirical results show the interest of density-based and hybrid dual-criterion cu...