# Coupling Learning of Complex Interactions

arXiv: https://arxiv.org/abs/2007.13534 · HTML: https://mlsanity.com/paper/2007.13534/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning](https://mlsanity.com/paper/2303.08046/) (2023)
- [Homophily Outlier Detection in Non-IID Categorical Data](https://mlsanity.com/paper/2103.11516/) (2021)
- [Deep Coupling Network For Multivariate Time Series Forecasting](https://mlsanity.com/paper/2402.15134/) (2024)
- [DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction](https://mlsanity.com/paper/2108.08170/) (2021)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling](https://mlsanity.com/paper/2305.08778/) (2023)
- [Recurrent Coupled Topic Modeling over Sequential Documents](https://mlsanity.com/paper/2106.13732/) (2021)
- [Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences](https://mlsanity.com/paper/2204.03571/) (2022)
- [Causal Discovery on Dependent Binary Data](https://mlsanity.com/paper/2412.20289/) (2024)

## Abstract (arXiv)

Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups.