arXiv research

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

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

Ongoing developments in neural network models are continually advancing the state of the art in terms of system accuracy. However, the predicted labels should not be regarded as the only core output; also important is a well-calibrated estimate of the prediction uncertainty. Such estimates and their calibration are cri…

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

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.

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

ResMem improves model generalization by explicitly memorizing residuals.

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is usually quantified using the performance of the regressors in local regions of the feature space around the test pattern. However, choosing …

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

We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each …

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…

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

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

A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features. The idea of extending this chaining methodology to multi-output regression has a…

AirRL uses RL to infer urban air quality from selected stations.

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

A method for predicting profit and loss distributions of complex financial portfolios using neural networks.

As an efficient and scalable graph neural network, GraphSAGE has enabled an inductive capability for inferring unseen nodes or graphs by aggregating subsampled local neighborhoods and by learning in a mini-batch gradient descent fashion. The neighborhood sampling used in GraphSAGE is effective in order to improve compu…

We focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space. Such problems often employ sparse priors, which promote models using a small subset of regressors. To increase statistical power, the so-c…

Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth…

This study analyzes the effects of lifting lockdowns on Brazil's COVID-19 spread.

Density-Regression improves deep uncertainty estimation with faster inference.

TRNN combines tensor geometry with neural network nonlinearity for HD data.

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regre…

We propose a new algorithm for estimating NARMAX models with $L_1$ regularization for models represented as a linear combination of basis functions. Due to the $L_1$-norm penalty the Lasso estimation tends to produce some coefficients that are exactly zero and hence gives interpretable models. The novelty of the contri…

Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for s…

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

A new method to predict uncertainties in trained neural networks.

In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $ε$-tube of the regressor and also assigns reward for the data points which lie inside of the $ε$-tube of…

In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses meta-regressors to determine which candidate configurations should be el…

Research forecasts electricity spot prices using stochastic volatility models.

The paper studies quadratic neural networks, proving existence of spurious minima and saddle points.

DTOR explains anomalies with rule-based explanations.

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

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…

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

Proposes a new test for validating multivariate dynamic regression models.

Toxicity analysis and prediction are of paramount importance to human health and environmental protection. Existing computational methods are built from a wide variety of descriptors and regressors, which makes their performance analysis difficult. For example, deep neural network (DNN), a successful approach in many o…

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.

A new neural network model predicts inflation and output gap more accurately.

FDN improves probabilistic regressors' adaptability to distribution shifts.

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…

Paper proves fair classification can be done via simple thresholding.

This paper compares machine learning models for pricing European options.

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

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…

Paper presents a self-adaptive learning model for robust classification and regression.