arXiv research

Compact audio event descriptor from regressor confidence scores.

We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.

Paper evaluates competence measures for DRS systems.

SPACR trains uncertainty-aware regressors directly within a single pass, improving efficiency and validity.

High-precision machine learning reduces particle physics simulations by orders of magnitude.

Paper develops a probabilistic regressor chain method using Monte Carlo methods.

Error correction for correlated regressors using compressed sensing.

Study shows $k$-NN regressor consistency in complex survey designs.

New TSER algorithms outperform existing methods in time series extrinsic regression.

The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.

Paper introduces $\ell$-DER for regression tasks using morphological operators and convex-concave procedure.

Proposes a new regularizer for multi-task regression using Wasserstein geometry.

A new algorithm estimates NARMAX models with L1 regularization using coordinate descent.

We solve a high-dimensional nonlinear model using Gaussian regressors.

Study improves flood loss risk models using historical data and rainfall data.

Proposes an ensemble loss function for robust regression.

Two new methods improve neural network regressor calibration.

Boosting ridge regression for high-dimensional data classification reduces computational cost and improves learning time.

Echo state network (ESN) is viewed as a temporal non-orthogonal expansion with pseudo-random parameters. Such expansions naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the variance of the tea…

Research forecasts electricity spot prices using stochastic volatility models.

DTOR explains anomalies with rule-based explanations.

We study nonlinear regression of real valued data in an individual sequence manner, where we provide results that are guaranteed to hold without any statistical assumptions. We address the convergence and undertraining issues of conventional nonlinear regression methods and introduce an algorithm that elegantly mitigat…

Paper reduces sample complexity for bilinear systems identification to nearly constant.

Novel algorithm identifies nonlinear Granger causal relationships using kernel ridge regression.

Proposes a new test for validating multivariate dynamic regression models.

The paper explores the tradeoff between fairness and accuracy in regression models.

This study examines the relationship between PLS and OLS regression using eigenvalue distributions.

Meta-learning improves hyperparameter tuning for XGBoost.

We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polyno…

FDN improves probabilistic regressors' adaptability to distribution shifts.

Paper introduces a new SVR model using a combined reward and penalty loss function.

Method selects valid IVs from a large set using clustering and test of overidentifying restrictions.

Overparameterized ensembles don't offer generalization benefits over single large models.

This paper proposes a fast and accurate method for sparse regression in the presence of missing data. The underlying statistical model encapsulates the low-dimensional structure of the incomplete data matrix and the sparsity of the regression coefficients, and the proposed algorithm jointly learns the low-dimensional s…

PAGER detects failures in deep regression models using a new framework.

Improved local feature attributions using neighbourhood reference distributions.

Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data. These regressors thus escape the curse of dimension when high-dimensional data has low intrinsic dimension (e.g. a manifold). We show that k-NN regression is also adaptive to intrinsic dimension. …

Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.

Adaptive LASSO improves model selection for functional geostatistical data.

Gaussian processes improve system identification models.

Markov boundary improves tabular prediction but not as expected.

Paper examines LASSO for high-dimensional predictive regression, improving its performance in forecasting unemployment.

Machine learning predicts Hodge numbers of Calabi-Yau four-folds.

Adaptive Gaussian kernel filtering with updated parameters.

In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regr…

The study improves Bitcoin price prediction using hybrid machine learning and enhances interpretability.

ResMem improves model generalization by explicitly memorizing residuals.

We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a natural regularized loss function which the RKHS embeddings minimise, providing an intuitive understanding of the embeddings and a justificatio…