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.
Paper reduces sample complexity for bilinear systems identification to nearly constant.
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.
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 …
High-precision machine learning reduces particle physics simulations by orders of magnitude.
Study shows -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…
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
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…
Paper introduces -DER for regression tasks using morphological operators and convex-concave procedure.
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…
Proposes a framework to quantify uncertainty in multi-step decision-making by LLMs.
Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters incl…
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 regularization for models represented as a linear combination of basis functions. Due to the -norm penalty the Lasso estimation tends to produce some coefficients that are exactly zero and hence gives interpretable models. The novelty of the contri…
Study improves flood loss risk models using historical data and rainfall data.
Boosting ridge regression for high-dimensional data classification reduces computational cost and improves learning time.
Extends RL to random stopping times, improving optimization.
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 -tube of the regressor and also assigns reward for the data points which lie inside of the -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…
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…
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.
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…
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…
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…
Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to …
PAGER detects failures in deep regression models using a new framework.
Improved local feature attributions using neighbourhood reference distributions.
OSGM uses online learning to adapt stepsize for faster convergence.
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.
The present paper proposes generalized Gaussian kernel adaptive filtering, where the kernel parameters are adaptive and data-driven. The Gaussian kernel is parametrized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of the scalar width parameter. These…
Adaptive LASSO improves model selection for functional geostatistical data.
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the labeled and unlabeled instances; and (ii) apply a k nearest neighbor regressor b…
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.