Dual-Criterion Curriculum Learning: Application to Temporal Data
arXiv:2603.23573v1 Announce Type: new 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
arXiv:2603.23573v1 Announce Type: new 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 curricula over loss-only baselines and standard non-CL training in this setting.
Executive Summary
This article proposes a novel framework called Dual-Criterion Curriculum Learning (DCCL) that combines loss-based and density-based criteria to assess instance-wise difficulty in training models. The DCCL framework is applied to the time-series forecasting task and shows promising results compared to standard One-Pass and Baby-Steps training schedules. The authors argue that their approach addresses the main bottleneck of effective learning by calibrating training-based evidence under data sparseness. The empirical results demonstrate the interest of density-based and hybrid dual-criterion curricula in this setting. Overall, the article presents a valuable contribution to the field of meta-learning and curriculum learning.
Key Points
- ▸ DCCL combines loss-based and density-based criteria to assess instance-wise difficulty
- ▸ The framework is applied to the time-series forecasting task
- ▸ Empirical results show the interest of density-based and hybrid dual-criterion curricula
Merits
Novel and Comprehensive Framework
The DCCL framework offers a novel and comprehensive approach to assessing instance-wise difficulty, addressing a significant bottleneck in effective learning.
Empirical Evidence
The article presents empirical results that demonstrate the interest of density-based and hybrid dual-criterion curricula in the time-series forecasting task.
Demerits
Limited Scope
The article focuses primarily on the time-series forecasting task, which may limit the generalizability of the results to other domains.
Lack of Theoretical Analysis
The article does not provide a detailed theoretical analysis of the DCCL framework, which may limit its understanding and application by researchers and practitioners.
Expert Commentary
The article presents a valuable contribution to the field of meta-learning and curriculum learning. The DCCL framework offers a novel and comprehensive approach to assessing instance-wise difficulty, addressing a significant bottleneck in effective learning. The empirical results demonstrate the interest of density-based and hybrid dual-criterion curricula in the time-series forecasting task. However, the article's focus on a specific domain and the lack of theoretical analysis may limit its generalizability and understanding. Nevertheless, the article provides a solid foundation for future research and applications of the DCCL framework.
Recommendations
- ✓ Future research should aim to extend the DCCL framework to other domains and provide a detailed theoretical analysis.
- ✓ Practitioners should consider applying the DCCL framework to their own datasets, particularly in areas where data sparseness is a significant challenge.
Sources
Original: arXiv - cs.LG
