Approximate Cross-Validation for Structured Models

• URL: http://arxiv.org/abs/2006.12669v2
• Date: Tue, 1 Dec 2020 17:37:42 GMT
• Title: Approximate Cross-Validation for Structured Models
• Authors: Soumya Ghosh and William T. Stephenson and Tin D. Nguyen and Sameer K. Deshpande and Tamara Broderick
• Abstract summary: Gold standard evaluation technique is structured cross-validation (CV) But CV here can be prohibitively slow due to the need to re-run already-expensive learning algorithms many times. Previous work has shown approximate cross-validation (ACV) methods provide a fast and provably accurate alternative.
• Score: 20.79997929155929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many modern data analyses benefit from explicitly modeling dependence structure in data -- such as measurements across time or space, ordered words in a sentence, or genes in a genome. A gold standard evaluation technique is structured cross-validation (CV), which leaves out some data subset (such as data within a time interval or data in a geographic region) in each fold. But CV here can be prohibitively slow due to the need to re-run already-expensive learning algorithms many times. Previous work has shown approximate cross-validation (ACV) methods provide a fast and provably accurate alternative in the setting of empirical risk minimization. But this existing ACV work is restricted to simpler models by the assumptions that (i) data across CV folds are independent and (ii) an exact initial model fit is available. In structured data analyses, both these assumptions are often untrue. In the present work, we address (i) by extending ACV to CV schemes with dependence structure between the folds. To address (ii), we verify -- both theoretically and empirically -- that ACV quality deteriorates smoothly with noise in the initial fit. We demonstrate the accuracy and computational benefits of our proposed methods on a diverse set of real-world applications.

Related papers

• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification [51.35500308126506]
  Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. We study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types.
  arXiv  Detail & Related papers  (2024-07-16T23:17:36Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• On the Performance of Empirical Risk Minimization with Smoothed Data [59.3428024282545]
  Empirical Risk Minimization (ERM) is able to achieve sublinear error whenever a class is learnable with iid data. We show that ERM is able to achieve sublinear error whenever a class is learnable with iid data.
  arXiv  Detail & Related papers  (2024-02-22T21:55:41Z)
• Iterative Approximate Cross-Validation [13.084578404699174]
  Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models. In this paper, we propose a new paradigm to efficiently approximate CV when the empirical risk minimization (ERM) problem is solved via an iterative first-order algorithm. Our new method extends existing guarantees for CV approximation to hold along the whole trajectory of the algorithm, including at convergence.
  arXiv  Detail & Related papers  (2023-03-05T17:56:08Z)
• Confidence intervals for the Cox model test error from cross-validation [91.3755431537592]
  Cross-validation (CV) is one of the most widely used techniques in statistical learning for estimating the test error of a model. Standard confidence intervals for test error using estimates from CV may have coverage below nominal levels. One way to this issue is by estimating the mean squared error of the prediction error instead using nested CV.
  arXiv  Detail & Related papers  (2022-01-26T06:40:43Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection [21.06860861548758]
  Cross Validation (CV) is the main workhorse for model selection. CV suffers a conservatively biased estimation, since some part of the limited data has to hold out for validation. CV tends to be extremely cumbersome, e.g., intolerant time-consuming, due to the repeated training procedures.
  arXiv  Detail & Related papers  (2020-12-24T16:11:53Z)
• Approximate Cross-Validation with Low-Rank Data in High Dimensions [35.74302895575951]
  Cross-validation is an important tool for model assessment. ACV methods can lose both speed and accuracy in high dimensions unless sparsity structure is present in the data. We develop a new algorithm for ACV that is fast and accurate in the presence of ALR data.
  arXiv  Detail & Related papers  (2020-08-24T16:34:05Z)
• Efficient implementations of echo state network cross-validation [0.0]
  Cross-Validation (CV) is still uncommon in time series modeling. We discuss CV of time series for predicting a concrete time interval of interest. We introduce an efficient algorithm for implementing them.
  arXiv  Detail & Related papers  (2020-06-19T13:49:43Z)
• Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation [1.7205106391379026]
  In machine learning one often assumes the data are independent when evaluating model performance. spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates. We propose a modified version of the CV method called spatial k-fold cross validation (SKCV) which provides a useful estimate for model prediction performance without optimistic bias due to SAC.
  arXiv  Detail & Related papers  (2020-05-28T19:55:18Z)

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.