# Dual-Criterion Curriculum Learning: Application to Temporal Data

arXiv: https://arxiv.org/abs/2603.23573 · HTML: https://mlsanity.com/paper/2603.23573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives](https://mlsanity.com/paper/1905.10847/) (2019)
- [Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1908.00262/) (2019)
- [An Analytical Theory of Curriculum Learning in Teacher-Student Networks](https://mlsanity.com/paper/2106.08068/) (2021)
- [Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning](https://mlsanity.com/paper/2402.18361/) (2024)
- [Flipped Classroom: Effective Teaching for Time Series Forecasting](https://mlsanity.com/paper/2210.08959/) (2022)
- [Curriculum Learning in Deep Neural Networks for Financial Forecasting](https://mlsanity.com/paper/1904.12887/) (2019)
- [Density-Based Dynamic Curriculum Learning for Intent Detection](https://mlsanity.com/paper/2108.10674/) (2021)
- [Curriculum learning for data-driven modeling of dynamical systems](https://mlsanity.com/paper/2112.08458/) (2021)
- [Data Distribution-based Curriculum Learning](https://mlsanity.com/paper/2402.07352/) (2024)

## Abstract (arXiv)

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.