# Parsimonious modeling with Information Filtering Networks

arXiv: https://arxiv.org/abs/1602.07349 · HTML: https://mlsanity.com/paper/1602.07349/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2016.

## Built on by (most cited first, 14 of 14)

- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series](https://mlsanity.com/paper/2203.03991/) (2022)
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- [Forecasting Financial Market Structure from Network Features using Machine Learning](https://mlsanity.com/paper/2110.11751/) (2021)
- [Information Filtering Networks: Theoretical Foundations, Generative Methodologies, and Real-World Applications](https://mlsanity.com/paper/2505.03812/) (2025)
- [Graph Regularized PCA](https://mlsanity.com/paper/2601.10199/) (2026)
- [Sparsistent filtering of comovement networks from high-dimensional data](https://mlsanity.com/paper/2101.09174/) (2021)
- [Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing](https://mlsanity.com/paper/2208.12614/) (2022)
- [Variable Clustering via Distributionally Robust Nodewise Regression](https://mlsanity.com/paper/2212.07944/) (2022)
- [Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanism](https://mlsanity.com/paper/2402.07735/) (2024)
- [A Novel approach to portfolio construction](https://mlsanity.com/paper/2602.03325/) (2026)
- [Compositional Sparsity as an Inductive Bias for Neural Architecture Design](https://mlsanity.com/paper/2605.14764/) (2026)

## Abstract (arXiv)

We introduce a methodology to construct parsimonious probabilistic models. This method makes use of Information Filtering Networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small sub-parts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust even for the estimation of inverse covariance of high-dimensional, noisy and short time-series. Applied to financial data our method results computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big datasets with large numbers of variables. Examples of practical application for forecasting, stress testing and risk allocation in financial systems are also provided.