mlsanityData to 7 Oct 2026

Discovering Nonlinear Relations with Minimum Predictive Information Regularization

arXiv 2001.01885 ↗First posted 7 Jan 2020cs.LG stat.MLcited by 16

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Abstract (arXiv)

Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem. In this work, we introduce a novel minimum predictive information regularization method to infer directional relations from time series, allowing deep learning models to discover nonlinear relations. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets, and discovers the directional relations in a video game environment and a heart-rate vs. breath-rate dataset.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jan 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 6; 2024: 4.

16 papers.

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  1. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 44
  2. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  3. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  4. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  5. 2021Learning interaction rules from multi-animal trajectories via augmented behavioral modelscited by 5
  6. 2024CausalMMM: Learning Causal Structure for Marketing Mix Modelingcited by 5
  7. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  8. 2022Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Culturescited by 2
  9. 2022Learning domain-specific causal discovery from time seriescited by 2
  10. 2023A Graph Dynamics Prior for Relational Inferencecited by 2
  11. 2023Causal Temporal Regime Structure Learningcited by 2
  12. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
4 more
  1. 2024Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state modelscited by 1
  2. 2024Bayesian Vector AutoRegression with Factorised Granger-Causal Graphscited by 1
  3. 2021An Interpretable Neural Network for Parameter Inferencecited by 0
  4. 2024A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivitycited by 0