Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets

• URL: http://arxiv.org/abs/2312.12786v2
• Date: Sat, 17 Aug 2024 05:27:52 GMT
• Title: Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets
• Authors: Ruzhang Zhao, Prosenjit Kundu, Arkajyoti Saha, Nilanjan Chatterjee,
• Abstract summary: We describe a transfer learning approach for building high-dimensional generalized linear models. We use data from a main study with detailed information on all predictors and an external, potentially much larger, study that has a more limited set of predictors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Development of comprehensive prediction models are often of great interest in many disciplines of science, but datasets with information on all desired features often have small sample sizes. We describe a transfer learning approach for building high-dimensional generalized linear models using data from a main study with detailed information on all predictors and an external, potentially much larger, study that has ascertained a more limited set of predictors. We propose using the external dataset to build a reduced model and then "transfer" the information on underlying parameters for the analysis of the main study through a set of calibration equations which can account for the study-specific effects of design variables. We then propose a penalized generalized method of moment framework for inference and a one-step estimation method that could be implemented using standard glmnet package. We develop asymptotic theory and conduct extensive simulation studies to investigate both predictive performance and post-selection inference properties of the proposed method. Finally, we illustrate an application of the proposed method for the development of risk models for five common diseases using the UK Biobank study, combining information on low-dimensional risk factors and high throughout proteomic biomarkers.

Related papers

• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• 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)
• Towards Biologically Plausible and Private Gene Expression Data Generation [47.72947816788821]
  Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. We initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data.
  arXiv  Detail & Related papers  (2024-02-07T14:39:11Z)
• Hypothesis Testing using Causal and Causal Variational Generative Models [0.0]
  Causal Gen and Causal Variational Gen can utilize nonparametric structural causal knowledge combined with a deep learning functional approximation. We show how, using a deliberate (non-random) split of training and testing data, these models can generalize better to similar, but out-of-distribution data points. We validate our methods on a synthetic pendulum dataset, as well as a trauma surgery ground level fall dataset.
  arXiv  Detail & Related papers  (2022-10-20T13:46:15Z)
• SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models [58.78043959556283]
  We study the behaviors of models under different fine-tuning methodologies, including full fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning methods. Our analysis uncovers that LoRA fine-tuning consistently exhibits significantly better OOD generalization performance than full fine-tuning across various scenarios.
  arXiv  Detail & Related papers  (2022-10-10T16:07:24Z)
• A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements [3.65211252467094]
  We present a data-driven correlation to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios. A material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve leaflet. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material properties learned implicitly from the data and naturally embedded in the network parameters.
  arXiv  Detail & Related papers  (2022-04-01T04:56:41Z)
• 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)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network [5.000272778136268]
  This study shows that the predictive coding (PC) and active inference (AIF) frameworks can develop better generalization by learning a prior distribution in a low dimensional latent state space. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data.
  arXiv  Detail & Related papers  (2020-05-27T06:43:59Z)

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.