Develops robust classification algorithms for positive-unlabeled data with noisy labels.
problem Learning from positive-unlabeled data with noise in positive labels.
method Explicitly models noise in positive labels and uses univariate transforms built on discriminative classifiers.
result Estimates class prior and posterior distributions robustly from noisy positives and unlabeled data.
Proposes a sparse linear classifier for classification with pairwise dependencies.
problem Classification accuracy is limited by tree-structured graphical models.
method Semi-parametric approach using sparse linear combination of univariate and bivariate log-transformed densities.
result SLB classifier is competitive with popular methods.
Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.
problem Optimal unimodal transformation of univariate model scores under linear loss functions.
method Proposes a sequential approach to estimate the optimal rectangular fit for observed samples with each new sample.
result Sequential approach achieves optimal efficiency with logarithmic time complexity per iteration.
W-Transformers use wavelets to improve time series forecasting.
problem Forecasting non-stationary time series with long-range dependencies.
method Wavelet-based transformer architecture.
result W-Transformers outperform baseline models on various time series datasets.
Study proposes a new portfolio selection method using non-Gaussian models and Esscher transform.
problem Portfolio selection with complex stock return structures and skewness, kurtosis.
method Multivariate non-Gaussian models (NTS and GH), Esscher transform for risk-neutral measure, simultaneous calibration of univariate log-returns and volatility.
result Demonstrated the effectiveness of the proposed models in fitting and selecting portfolios.
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
Recent advances in statistical theory, together with advances in the computational power of computers, provide alternative methods to do mass-univariate hypothesis testing in which a large number of univariate tests, can be properly used to compare MEEG data at a large number of time-frequency points and scalp location…
NAF combines neural networks with autoregressive models for better density estimation.
problem Improving density estimation and speech synthesis speed.
method Generalizes autoregressive models using neural networks for invertible transformations.
result NAF is a universal approximator for continuous probability distributions and outperforms IAF.
New graph Fourier transform distinguishes directions in multi-dimensional signals.
problem Existing graph Fourier transform fails to distinguish directions in multi-dimensional signals.
method Algebraic properties of Cartesian products rearrange 1-D spectra into multi-dimensional frequency domain.
result Solves multi-valuedness of spectra and enables directional frequency analysis.
AdaPTS adapts univariate FMs for multivariate time series forecasting.
problem Challenges in managing feature dependencies and uncertainty quantification in multivariate time series forecasting.
method Adapters that transform multivariate inputs into a latent space and apply univariate FMs independently to each dimension.
result AdaPTS enhances forecasting accuracy and uncertainty quantification compared to baseline methods.
In this paper we consider Fourier transform techniques to efficiently compute the Value-at-Risk and the Conditional Value-at-Risk of an arbitrary loss random variable, characterized by having a computable generalized characteristic function. We exploit the property of these risk measures of being the solution of an ele…
Proposes a new feature preprocessing method using kernel density integral transformation.
problem Feature preprocessing for tabular data in machine learning and statistics.
method Kernel density integral transformation as a drop-in replacement or improved alternative to min-max scaling and quantile transformation.
result Frequently outperforms min-max scaling and quantile transformation with hyperparameter tuning.
Proposes a new method for PDF estimation using RBIG transforms.
problem Challenging task of multidimensional PDF estimation.
method Rotation-based Iterative Gaussianization (RBIG) transforms.
result RBIG transforms can be used to estimate unknown PDFs efficiently.
The paper explores how generative networks can transform noise distributions into other distributions.
problem Transforming noise distributions into desired distributions using generative networks.
method Developed a space-filling function for ReLU networks and provided efficient methods for univariate uniform to normal distribution transformations.
result Optimal construction for ReLU networks to increase noise dimensionality and efficient methods for distribution transformations.
Kolmogorov-Arnold Networks achieve optimal convergence rates in nonparametric regression.
problem Nonparametric function approximation in multivariate settings.
method Structured additive and multiplicative KANs using B-splines.
result Achieve minimax-optimal convergence rate O(n−2r/(2r+1)) for Sobolev space functions. The univariate piecing-together approach (PT) fits a univariate generalized Pareto distribution (GPD) to the upper tail of a given distribution function in a continuous manner. We propose a multivariate extension. First it is shown that an arbitrary copula is in the domain of attraction of a multivariate extreme value …
FEDformer combines Transformer with seasonal-trend decomposition for efficient long-term forecasting.
problem Transformer's inefficiency and inability to capture global time series views.
method Combines seasonal-trend decomposition with Transformer, exploiting Fourier basis for frequency enhancement.
result Reduces prediction error by 14.8% and 22.6% for multivariate and univariate time series, respectively.
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
Meta algorithm solves multivariate optimization using univariate optimizers.
problem Multivariate global optimization problems.
method Meta algorithm combining univariate global optimizers.
result Meta algorithm provides robust regret guarantees.
LLT transforms time series features based on linear laws.
problem Classifying univariate and multivariate time series.
method Time-delay embedding, spectral decomposition, and feature transformation.
result Transformed features improve classification accuracy.
Unified framework for variational inference with copulas.
problem Hierarchical Bayesian models with continuous, non-Gaussian hidden variables.
method Semiparametric and automated variational Gaussian copula approach.
result Preserves multivariate posterior dependence and provides flexibility in univariate marginals.
Study evaluates deep learning models for cryptocurrency price prediction.
problem Accurate cryptocurrency price forecasting models are needed due to market volatility.
method Reviewed and evaluated deep learning models including LSTM, CNN, and Transformer.
result Convolutional LSTM with multivariate approach provides best prediction accuracy.
Characterizes functions representable by infinite-width ReLU networks with bounded weights.
problem Understanding function representation in overparameterized neural networks.
method Analyzes functions in Ws,1(R) spaces and their Radon transform. result All functions in Ws,1(R) can be represented with bounded norm. This study compares multivariate vs univariate machine learning for multi-output regression.
problem When to use multivariate ensemble techniques over separate univariate models.
method Comparative analysis of different multivariate approaches for multi-output regression.
result Multivariate ensemble techniques outperform separate univariate models in simulations.
NonSTOP predicts nonstationary time series with improved performance.
problem Handling nonstationary artifacts in time series data.
method Applying transformations to time series in the learning with experts setting.
result Improved prediction performance through transformations and follow-the-leader analysis.
Small neural networks embed arbitrary metric spaces into Gaussian mixtures.
problem Embedding arbitrary metric spaces into a fixed space with low distortion.
method Probabilistic transformers of small depth and width.
result Embeddings with low metric distortion for various metric spaces.
This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.
problem Improving multi-label ranking performance while maintaining consistency.
method Systematic study of consistency and generalization error bounds for learning algorithms, proposing a reweighted univariate loss.
result Inconsistent pairwise losses can lead to better performance than consistent univariate losses in practice.
Timer-XL predicts multidimensional time series using a unified Transformer approach.
problem Unified time series forecasting across various tasks and contexts.
method Decoder-only Transformers with a universal TimeAttention mechanism and deft position embedding.
result State-of-the-art performance across multiple forecasting benchmarks.
Paper proposes PMformer for better cryptocurrency price forecasting.
problem Huge volatility and trade-off between univariate and multivariate models.
method Partial-multivariate approach using PMformer.
result PMformer achieves significant statistical accuracy in forecasting.
New adaptive tests improve statistical dependence detection.
problem Testing statistical dependence between multivariate variables.
method Adaptive nonlinear monotonic transformations of distances.
result Empirical tests outperform existing methods.
Study compares univariate vs multivariate models for electricity price forecasting.
problem Optimal model structure for short-term electricity price forecasting.
method Comprehensive empirical study comparing univariate and multivariate modeling frameworks.
result Multivariate models do not uniformly outperform univariate models across all datasets, seasons, or hours.
Improved time series classification with GRU-FCN model.
problem Time series classification challenges.
method Hybrid LSTM-GRU model for univariate time series classification.
result GRU-FCN model outperforms state-of-the-art models.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
problem Assessing risks of late-frost and drought in Bavaria due to climate change.
method Used vine copula models for non-Gaussian and asymmetric dependencies, with univariate and bivariate regression analyses.
result Identified 'at-risk' regions for forest adaptation.
New model predicts univariate and multivariate time series with improved accuracy.
problem Complex patterns in univariate and multivariate time series forecasting.
method Uses autoregressive convolutional recurrent neural network with feature extraction and recurrent encoder.
result Outperforms existing architectures in multivariate time series datasets.
Deep learning methods improve time series forecasting by optimizing lag selection.
problem Optimizing the number of lags for accurate univariate time series forecasting.
method Empirical analysis of deep learning methods trained on multiple time series datasets.
result Excessively small or large lag sizes negatively impact forecasting performance.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
problem Need for research in multivariate quantiles, especially for bivariate responses.
method Constructs bivariate (conditional) quantiles using vine copula based bivariate regression model with a novel tree sequence graph structure.
result Avoids typical shortfalls of regression like transformations, interactions, collinearity, and quantile crossings.
Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, F measure, AUC, and so on. In multivariate tests, comparison is done using multiple measures simultaneously. For example, error is the sum of false positives and false negatives…
A new method calibrates scientific models by adding randomness to their predictions.
problem Current scientific foundation models lack calibrated uncertainty.
method Stochastic Attention, which randomizes attention weights using multinomial samples.
result Stochastic Attention achieves the strongest native calibration and sharpest prediction intervals.
This paper introduces a novel recalibration method for multivariate forecasts.
problem Multivariate calibration for potentially misspecified models.
method Local mappings between marginal probability integral transform values and observed space, using K-nearest neighbors or normalizing flows.
result Demonstrated effectiveness on currency exchange rate and childhood malnutrition data.
Flexible copula model using implicit generative neural networks.
problem Limited flexibility of parametric copulas and curse of dimensionality in non-parametric methods.
method Implicit generative neural networks to model high-dimensional copula distributions with unspecified marginals.
result Demonstrated flexibility and performance on various datasets.
Proposes a method to partition univariate data into unimodal subsets.
problem Partitioning univariate multimodal data into unimodal subsets.
method Recursive splitting around valley points of the data density using properties of critical points on the convex hull of the ecdf plot.
result Obtains a hierarchical statistical model of the initial dataset as a mixture of UMMs.
Study active learning of PTFs with derivative access.
problem Active learning of polynomial threshold functions (PTFs).
method Algorithm for active learning degree-d univariate PTFs with derivative access. result Computational efficient algorithm for active learning degree-d univariate PTFs. Deep reinforcement learning models win trading games on time series data.
problem Optimizing trading strategies for time series data.
method Deep Q-learning models (GRU, LSTM, CNN, MLP) trained on idealized trading games.
result Models can find profitable trading strategies for both univariate and bivariate time series data.
AI learns to classify and represent univariate distributions in a 2D latent space.
problem Classifying and representing univariate empirical distributions.
method Unsupervised beta variational autoencoder (beta-VAE) to separate and represent distributions in a 2D latent space.
result The latent space representation separates distributions of different shapes while overlapping similar ones.
GRM models k-way dependencies in univariate exponential families.
problem Modeling dependencies between variable sets of size k > 2.
method Taking k-th root of sufficient statistics for univariate exponential families.
result GRM models for Poisson and exponential families have no and only slight restrictions on parameters, respectively.
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taki…
New method detects bearing faults using multivariate statistical process control.
problem Early detection of bearing faults in rotating machinery.
method Multivariate statistical process control charts applied to Fourier transform features of fixed-time batches.
result Effectiveness in detecting bearing faults across different conditions.
New simulations advise caution in choosing principal components for multivariate functional data.
problem Inaccurate selection of principal components in multivariate functional data.
method Extensive simulations investigating the reliability of percentage of variance explained thresholds.
result Conventional threshold methods may fail to accurately explain overall variance in multivariate functional data.