Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

• URL: http://arxiv.org/abs/2106.01474v2
• Date: Tue, 23 May 2023 18:48:37 GMT
• Title: Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning
• Authors: Chengchun Shi, Yunzhe Zhou and Lexin Li
• Abstract summary: We propose a new hypothesis testing method for directed acyclic graph (DAG) We build the test based on some highly flexible neural networks learners. We demonstrate the efficacy of the test through simulations and a brain connectivity network analysis.
• Score: 7.623002328386318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We build the test based on some highly flexible neural networks learners. We establish the asymptotic guarantees of the test, while allowing either the number of subjects or the number of time points for each subject to diverge to infinity. We demonstrate the efficacy of the test through simulations and a brain connectivity network analysis.

Related papers

• Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models [71.39421638547164]
  We propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOD Detection, dubbed as PGR-MOOD and hinges on three innovations.
  arXiv  Detail & Related papers  (2024-04-24T03:25:53Z)
• Hypothesis-Driven Deep Learning for Out of Distribution Detection [0.8191518216608217]
  We propose a hypothesis-driven approach to quantify whether a new sample is InD or OoD. We adapt our method to detect an unseen sample of bacteria to a trained deep learning model, and show that it reveals interpretable differences between InD and OoD latent responses.
  arXiv  Detail & Related papers  (2024-03-21T01:06:47Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts [143.14128737978342]
  Test-time adaptation, an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. Recent progress in this paradigm highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference.
  arXiv  Detail & Related papers  (2023-03-27T16:32:21Z)
• Model-Free Sequential Testing for Conditional Independence via Testing by Betting [8.293345261434943]
  The proposed test allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dependency structure. We allow the processing of data points online as soon as they arrive and stop data acquisition once significant results are detected.
  arXiv  Detail & Related papers  (2022-10-01T20:05:33Z)
• Conditional Independence Testing via Latent Representation Learning [2.566492438263125]
  LCIT (Latent representation based Conditional Independence Test) is a novel non-parametric method for conditional independence testing based on representation learning. Our main contribution involves proposing a generative framework in which to test for the independence between X and Y given Z.
  arXiv  Detail & Related papers  (2022-09-04T07:16:03Z)
• A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data [0.26651200086513094]
  Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions.
  arXiv  Detail & Related papers  (2022-06-09T08:56:12Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Learning continuous models for continuous physics [94.42705784823997]
  We develop a test based on numerical analysis theory to validate machine learning models for science and engineering applications. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.
  arXiv  Detail & Related papers  (2022-02-17T07:56:46Z)
• NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge [38.2191204484905]
  We propose a score-based DAG structure learning method for time-series data. The proposed method extends nonparametric NOTEARS, a recent continuous optimization approach for learning nonparametric instantaneous DAGs.
  arXiv  Detail & Related papers  (2021-09-09T14:08:09Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

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.