The m-connecting imset and factorization for ADMG models

• URL: http://arxiv.org/abs/2207.08963v1
• Date: Mon, 18 Jul 2022 22:29:15 GMT
• Title: The m-connecting imset and factorization for ADMG models
• Authors: Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
• Abstract summary: Acyclic directed mixed graph (ADMG) models characterize margins of DAG models. ADMG models have not seen wide-spread use due to their complexity and a shortage of statistical tools for their analysis. We introduce the m-connecting imset which provides an alternative representation for the independence models induced by ADMGs.
• Score: 10.839217026568784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learning and inference. Unfortunately, these models are not closed under marginalization, making them poorly equipped to handle systems with latent confounding. Acyclic directed mixed graph (ADMG) models characterize margins of DAG models, making them far better suited to handle such systems. However, ADMG models have not seen wide-spread use due to their complexity and a shortage of statistical tools for their analysis. In this paper, we introduce the m-connecting imset which provides an alternative representation for the independence models induced by ADMGs. Furthermore, we define the m-connecting factorization criterion for ADMG models, characterized by a single equation, and prove its equivalence to the global Markov property. The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models. We demonstrate the usefulness of these tools by formulating and evaluating a consistent scoring criterion with a closed form solution.

Related papers

• Model aggregation: minimizing empirical variance outperforms minimizing empirical error [0.29008108937701327]
  We propose a data-driven framework that aggregates predictions from diverse models into a single, more accurate output. It is non-intrusive - treating models as black-box functions - model-agnostic, requires minimal assumptions, and can combine outputs from a wide range of models. We show how it successfully integrates traditional solvers with machine learning models to improve both robustness and accuracy.
  arXiv  Detail & Related papers  (2024-09-25T18:33:21Z)
• Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models [42.17166746027585]
  We introduce a bidirectional weighted graph-based framework to learn factorized attributes and their interrelations within complex data. Specifically, we propose a $beta$-VAE based module to extract factors as the initial nodes of the graph. By integrating these complementary modules, our model successfully achieves fine-grained, practical and unsupervised disentanglement.
  arXiv  Detail & Related papers  (2024-07-26T15:32:21Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning [0.0]
  An interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions.
  arXiv  Detail & Related papers  (2024-04-08T18:16:22Z)
• Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL [57.745700271150454]
  We study the sample complexity of reinforcement learning in Mean-Field Games (MFGs) with model-based function approximation. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity.
  arXiv  Detail & Related papers  (2024-02-08T14:54:47Z)
• AMR Parsing with Causal Hierarchical Attention and Pointers [54.382865897298046]
  We introduce new target forms of AMR parsing and a novel model, CHAP, which is equipped with causal hierarchical attention and the pointer mechanism. Experiments show that our model outperforms baseline models on four out of five benchmarks in the setting of no additional data.
  arXiv  Detail & Related papers  (2023-10-18T13:44:26Z)
• Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models [0.5801044612920815]
  This paper proposes a framework that expands the potential of data-driven approaches for DCM. It includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment. A case study demonstrates the potential of this framework for discrete choice analysis.
  arXiv  Detail & Related papers  (2023-05-30T12:53:55Z)
• A Guide for Practical Use of ADMG Causal Data Augmentation [0.0]
  Causal data augmentation strategies have been pointed out as a solution to handle these challenges. This paper experimentally analyzed the ADMG causal augmentation method considering different settings.
  arXiv  Detail & Related papers  (2023-04-03T09:31:13Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles [78.6363825307044]
  We propose Explainable Matrix (ExMatrix), a novel visualization method for Random Forest (RF) interpretability. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.
  arXiv  Detail & Related papers  (2020-05-08T21:03:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.