A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Subject Cross Validation improves Human Activity Recognition performance by up to 16%.
CNN improves spatiotemporal emotion recognition from EEG during music listening.
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didacti…
Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to raise awareness on e…
Unified model combines neural networks and dictionary learning for clinical predictions from brain data.
This paper addresses the problem of emotion recognition from physiological signals. Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. The inter-subject variability and the personalization effect are thoroughly investigated, through trial-based and su…
Geometry-aware models improve cross-subject EEG decoding accuracy.
A novel validation method improves feature importance analysis in subject-specific ML models.
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated fr…
A new method improves EEG classification across subjects efficiently.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we prove sanity-check bounds in the spirit of \cite{KR99} \textquotedblleft\textit{bounds showing that the worst-case error of this estimate is not m…
A graph-based model aligns unaligned fMRI data across subjects efficiently.
Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spect…
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for stable predictors in the context of risk assessment. The notion of stability has been first introduced by \cite{DEWA79} and extended by \cite{KEA95}, \cite{BE01} and \cite{KUNIY02} to characterize cla…
A fast bootstrap method estimates cross-validation standard error.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for subagged estimators, both for classification and regressor. General loss functions and class of predictors with both finite and infinite VC-dimension are considered. We slightly generalize the formali…
We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting. We formulate such principles as a bi…
When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed…
New cross-validation methods for Gaussian process regression with efficient gradient computation.
Improves test set performance and reduces out-of-sample disappointment for unstable models.
A new method improves super learner validation efficiency.
Cross-validation estimates model performance on unseen data, not training data.
The paper develops a cross-validation method for improving signal denoising techniques.
Optimizes Lasso hyperparameters using leave-one-out CV.
Efficient cross-validation method for Echo State Networks reduces validation time complexity.
The paper improves confidence intervals for test error using cross-validation.
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experiments. Using observational data to answer causal questions is risky: subjects who receive different treatments also differ in other ways that a…
CPTD improves prediction intervals in time series regression with cross-sectional data.
Framework integrates brain connectivity data for clinical predictions.
A new cross-validation method reduces redundancy and improves model performance.
Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that the cross-validation risk is differentiable with respect to the hyperparameters and training data for …
Cross-validation methods help learn dynamical systems from data.
This text is a survey on cross-validation. We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family. For the risk estimation problem, we compute the bias (which can also be …
Bayesian models use marginal likelihood; non-Bayesian use cross-validation, shown equivalent.
This note introduces the method of cross-conformal prediction, which is a hybrid of the methods of inductive conformal prediction and cross-validation, and studies its validity and predictive efficiency empirically.
Cross validation residuals are well known for the ordinary least squares model. Here leave-M-out cross validation is extended to generalised least squares. The relationship between cross validation residuals and Cook's distance is demonstrated, in terms of an approximation to the difference in the generalised residual …
In this paper, we introduce a new concept of stability for cross-validation, called the -stability, and use it as a new perspective to build the general theory for cross-validation. The -stability mathematically connects the generalization ability and the stability of…
Corrects a mistake in -block cross-validation consistency claim.
SA-GAN improves HAR model performance across new users.
Proposes a new cross-validation method to estimate model performance.
With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses nonparametric testing coupled with sequential analysis to determine the bes…
We speed up Gaussian process cross-validation calculations and improve model diagnostics.
Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recen…
Inter-subject variability between individuals poses a challenge in inter-subject brain signal analysis problems. A new algorithm for subject-selection based on clustering covariance matrices on a Riemannian manifold is proposed. After unsupervised selection of the subsets of relevant subjects, data in a cluster is mapp…
Estimates statistical shifts across subjects for EEG-based mental workload assessment.
A new method SLIDE ensures fairness in AI models.
Cross-validation is one of the most popular model selection methods in statistics and machine learning. Despite its wide applicability, traditional cross validation methods tend to select overfitting models, due to the ignorance of the uncertainty in the testing sample. We develop a new, statistically principled infere…