arXiv research

A method to select validation data from a dataset using statistical criteria.

New criteria distinguish cause from effect in data, overcoming statistical limitations.

Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correl…

A government has to finance a risk for its population. It shares the charges among the population with a fixed scale based on economic criteria. Various organisms have to collect and to redistribute fairly the subsidies. Under these conditions, when the size of the organisms is varied, the distribution's laws of the cr…

The paper integrates statistical significance and discriminative power in pattern discovery.

The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational crite…

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…

The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. ID3, C4.5 and CART are classical decision tree algorithms and the split criteria they…

The paper proves concentration inequalities for two-sample rank processes and applies them to ranking performance criteria.

Paper proposes a new method to compare classifiers across multiple datasets.

Many statistical models are given in the form of non-normalized densities with an intractable normalization constant. Since maximum likelihood estimation is computationally intensive for these models, several estimation methods have been developed which do not require explicit computation of the normalization constant,…

New method prevents classifiers from relying on spurious correlations.

The sBIC outperforms other model selection criteria in LDA topic modeling.

Unified framework for disentangled representations using mechanistic independence.

Study finds no statistically significant trading edge in MNQ futures signals from OHLCV data.

The paper addresses ill-conditioning in large spatial data, proposing solutions for prediction and likelihood estimation.

Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, credit risk screening, image ranking or media memorability. In this article, we systematically review different types…

The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points. Statistical efficiency is measured by optimality criteria, including A(verage), D(eterminant), T(race)…

New methods improve feature extraction and representation quality in supervised and unsupervised DR.

New learnability criteria for non-iid processes equivalent to online learning.

LS improves model selection for singular statistical models.

Based on criteria of mathematical simplicity and consistency with empirical market data, a stochastic volatility model is constructed, the volatility process being driven by fractional noise. Price return statistics and asymptotic behavior are derived from the model and compared with data. Deviations from Black-Scholes…

In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g., to obtain labels/responses, for a linear regression task. Many statistical criteria have been proposed for choosing the optimal design, with popular choices including A…

Paper discusses prediction errors for penalized regressions using GAMP and LOOCV.

Fairmetrics evaluates fairness in ML models for specific groups.

Unified framework for risk-aware policy learning in contextual bandits.

Latent DiTs improve data distribution recovery and inference efficiency under low-dimensional latent space.

Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distillin…

New criteria improve imputation model selection using MOO.

Researchers analyze a new neural network training method.

The authors argue against the classification of forecasting methods as machine learning or statistical.

New MMD estimators detect differences in missing paired data.

Introduces principal fairness for fair decision-making.

New estimators for causal effects in DAGs with hidden variables, addressing computational and statistical challenges.

We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures. Random matrices play the role of free random variables in this context so the method we…

The paper derives an equation linking WAIC and WBIC for singular models.

A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statist…

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is a…

Unified statistical framework for LSTM model selection.

We give some general criteria of being a homeomorphism for continuous mappings of topological manifolds, as well as criteria of being a diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we apply these criteria to the problems arising in two- and three-dimensional grid generation.

BayesBoost combines boosting and Bayesian methods for linear mixed models, improving uncertainty estimation and variable selection.

The paper explores fairness in credit scoring using machine learning.

Framework tests group fairness in machine learning models.

New method finds optimal hyperparameters for multiple tasks and criteria.

Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairn…

Jackknife variance estimation validated for generalized U-statistics.

New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.