New method for MMD with unequal sample sizes improves test power.
problem Existing MMD methods assume equal sample sizes, discarding valuable data.
method Extended generalized U-statistics to handle unequal sample sizes.
result New asymptotic distributions and power optimization for MMD with unequal sample sizes.
A new HL-SVR approach handles unequal sample sizes in SVR for engineering data modeling.
problem SVR assumes equal sample sizes, but unequal sizes are common in engineering.
method HL-SVR combines low-level SVR for larger samples and high-level SVR for smaller samples.
result HL-SVR produces more accurate predictions than conventional SVR.
Research tackles unequal length time series for classification.
problem Unequal length time series in real-world data.
method Identified and evaluated two classes of unequal length mechanisms.
result Practical recommendations for handling unequal length time series.
A test for distinguishing equal distributions from close ones with unequal samples.
problem Testing closeness of two discrete distributions with unequal sized samples.
method Describes a test for distinguishing equal distributions from close ones with unequal samples drawn from each.
result The test is successful with high probability under certain conditions on sample sizes.
Hexagonal tilings minimize perimeter with unequal volumes.
problem Finding optimal tessellations with unequal cell volumes.
method Minimizing perimeter functionals for different classes of problems.
result Hexagonal tilings are optimal among partitions with almost equal areas.
Deep learning models interpret patient outcomes better with time aggregation.
problem Chronic ambulatory care data challenges for deep learning models.
method Time-distributed-dense layers combined with GRUs for generalization, clinical interpretation framework.
result Time-distributed-dense layers with GRUs produce the most generalizable models.
A new classification rule for FDA improves classification performance by accounting for unequal covariance matrices.
problem Unequal covariance matrices in practical situations affect the performance of FDA and its variants.
method Proposes a novel classification rule for FDA that accounts for unequal covariance matrices, applicable to many FDA variants.
result The new classification rule improves classification performance compared to original FDA and variants.
Bayesian method estimates sparse precision matrices with unequal shrinkage.
problem Estimating high-dimensional sparse precision matrices with unequal shrinkage.
method Bayesian framework with mixture of Laplace priors and EM algorithm for computation.
result Optimal error rates and selection consistency for sparse structure recovery.
Paper proposes a new importance sampling method for reducing variance.
problem Reducing variance in importance sampling when training and testing data come from different distributions.
method A new variant of importance sampling that reduces variance by orders of magnitude.
result The new estimator can improve estimates of treatment effectiveness using limited data.
This study examines the topology of singularities in optimal semicouplings between unequal spaces.
problem Topology of singularities in optimal semicouplings between unequal spaces.
method Continuous strong deformation retracts and Uniform Halfspace condition.
result Homotopy-reductions from a source space onto singularities of c-optimal semicouplings. In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natural approach in this context consists in using survey schemes and substituting the "full data" stati…
Paper tackles robust estimation of tree-structured Ising models without side information.
problem Learning tree-structured Ising models with flipped signs of variables.
method Proves unidentifiability, proposes an algorithm with logarithmic sample complexity and polynomial run-time complexity.
result Empirically demonstrates robustness of proposed algorithm in the flipped signs setting.
A statistical generalization is made of microeconomics in the spirit of going from classical to statistical mechanics. The price and quantity of every commodity1 traded in the market, at each instant of time, is considered to be an independent random variable: all prices and quantities are considered to be stochastic p…
Developing tools for computing string amplitudes with hyperbolic vertices.
problem Computing off-shell string amplitudes with new vertices.
method Constructing local coordinates and investigating limits for hyperbolic three-string vertex.
result Derived conservation laws and performed sample computations.
MobileNet CNN achieves high accuracy in skin disease classification on Android.
problem Skin disease classification using smartphone technology.
method Transfer learning on MobileNet, imbalanced dataset handling (sampling and preprocessing), and data augmentation.
result Oversampling and data augmentation on preprocessing input data achieved 94.4% accuracy.
Cyclical MCMC tackles high-dimensional multimodal distributions, showing convergence under certain conditions.
problem High-dimensional multimodal posterior distributions in deep learning.
method Cyclical MCMC framework that tracks tempered versions of the target distribution over time.
result Cyclical MCMC converges to the target distribution under fast mixing kernels but fails in slow mixing cases.
Paper finds gender classification accuracy varies by skin type, not ethnicity.
problem Unequal performance of face classification services across skin types and genders.
method Stability experiments, image manipulation, and post-hoc explanation techniques.
result Lip, eye, and cheek structure differences, not skin type, cause gender classification discrepancies.
New method uses causal thinking to make AI fairer decisions.
problem Designing fair machine learning models that treat equal individuals equally and unequals unequally.
method Rank-preserving interventional distributions and warping method.
result Warping method effectively identifies discriminated individuals and mitigates unfairness.
A new method approximates deep neural networks using Kalman Filters.
problem Approximating deep neural networks with limited data.
method Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN).
result MEnKF-ANN can adequately approximate LSTM networks and provide uncertainty estimates.
The paper analyzes trade dynamics among G7 countries, revealing unequal exchange and degenerate equilibrium states.
problem Unequal exchange and degenerate equilibrium states in international trade among G7 countries.
method Analysis based on a model of international trade with supply and demand structures.
result Found relative equilibrium price vector is very degenerate, indicating unequal exchange.
New algorithm clusters Gaussian mixtures with unknown covariance efficiently.
problem Clustering data from a mixture of Gaussians with unknown covariance.
method Developed an efficient spectral algorithm based on a Max-Cut integer program.
result Achieves optimal misclassification rate with quadratic sample size.
Combines BART and Gaussian process for spatial covariate prediction with uncertainty.
problem Improving spatial prediction models with nonlinear and interaction covariates.
method Bayesian Additive Regression Trees (BART) combined with Gaussian process for spatial dependence.
result Effective in reducing computational burden through INLA and MCMC.
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
Sharp spectral theorems and isoperimetric inequalities for manifolds with nonnegative Ricci curvature.
problem Understanding the geometry and topology of manifolds with nonnegative Ricci curvature.
method New spectral inequalities and isoperimetric problems involving unequal weights and warped bubbles.
result Sharp spectral and isoperimetric bounds for manifolds with nonnegative Ricci curvature.
Optimal testing of discrete distributions with high probability, achieving sample complexity bounds.
problem Testing discrete distributions with high probability accuracy.
method Characterizing sample complexity as a function of parameters like δ, providing sample-optimal testers.
result Optimal algorithms for closeness and independence testing, achieving within constant factors of information-theoretic lower bounds.
The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.
problem Inaccurate A/B testing results due to non-normal data and unequal sample sizes.
method Derives explicit formulas for minimum sample size and introduces an Edgeworth-based correction.
result Corrected method improves reliability of A/B testing in real-world conditions.
Study shows unequal success of membership inference attacks across different groups.
problem Unequal success of membership inference attacks across different groups.
method Established conditions for preventing MIAs and derived connections to fairness and differential privacy.
result Estimating disparate vulnerability to MIAs can lead to overestimation; suitable attacks and statistical framework provided.
A new portfolio method using quantum mechanics improves risk diversification.
problem Improving risk-based portfolio construction methods for multi-asset portfolios.
method Schrödinger principal component analysis applied to extract common factors from asset fluctuations.
result The proposed method outperforms conventional risk parity and other risk diversification methods.
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
problem Asymmetric binary classification problems with unequal error severities.
method Develops TUBE-CS algorithm to bridge cost-sensitive and Neyman-Pearson paradigms.
result High-probability control of population type I error.
Robust fuzzy clustering for EEG driver alertness with outlier detection.
problem Ambiguous state boundaries in multivariate time series data.
method RFCPCA, a robust fuzzy subspace-clustering method for MTS.
result RFCPCA improves clustering accuracy and characterizes uncertainty and outliers in MTS.
Paper studies ranking from noisy comparisons with minimal assumptions.
problem Finding exact ranking from noisy comparisons with minimal assumptions.
method Adaptive comparisons, lower and upper bounds derivation.
result Nearly optimal pairwise ranking algorithms and extensions to listwise ranking.
Study examines how taxes affect wealth inequality in economic models.
problem Reducing economic inequality in models of economic activity.
method Examined Artificial Chemistry models and various tax measures.
result Effective tax measures can reduce economic inequality.
Bayesian approach learns linear networks from high-dimensional data.
problem Learning high-dimensional linear Bayesian networks.
method Iterative estimation of topological ordering and parents using inverse partial covariance matrix with Bayesian regularization.
result The method successfully recovers network structure under certain conditions.
Paper measures asymmetric fear network connectedness for risk prediction.
problem Predicting macroeconomic conditions and economic uncertainty.
method Forward-looking measures from bank option prices.
result Asymmetric network structure predicts economic conditions.
This Chapter reviews statistical models for the probability distribution of money developed in the econophysics literature since the late 1990s. In these models, economic transactions are modeled as random transfers of money between the agents in payment for goods and services. Starting from the initially equal distrib…
The covering spectrum is a geometric invariant of a Riemannian manifold, more generally of a metric space, that measures the size of its one-dimensional holes by isolating a portion of the length spectrum. In a previous paper we demonstrated that the covering spectrum is not a spectral invariant of a manifold in dimens…
Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.
problem Improving classification accuracy in partially classified samples with missing data.
method Generative model framework with missing-data mechanism, Bayes rule allocation.
result Bayes rule classifier with missing-data mechanism outperforms fully supervised classifiers in various conditions.
New approach detects racial segregation patterns in machine learning systems.
problem Challenges of fairness in machine learning systems due to racial identity.
method Unsupervised learning to detect patterns of segregation.
result Mitigates root cause of social disparities without reifying race.
A new framework for robust transfer learning that avoids negative transfer in domains with unequal information.
problem Negative transfer in unsupervised domain adaptation, especially when source and target domains have different levels of informativeness.
method Decision-theoretic framework based on Le Cam's theory of statistical experiments, using constructive approximations to replace strict invariance with directional simulability.
result Le Cam Distortion achieves near-perfect frequency estimation and zero source utility loss in various domains, demonstrating superior performance compared to traditional methods.
FROST speeds up and stabilizes one-shot semi-supervised learning.
problem Slow training and sensitivity to labeled data choices in semi-supervised learning.
method Combines semi-supervised learning with a one-stage, single network self-training approach.
result FROST trains up to 10x faster and is more robust to labeled data choices.
Model uses HS-FP framework for South African asset allocation.
problem Develops a flexible non-parametric asset allocation model for South African markets.
method Historical Simulation with Flexible Probabilities (HS-FP) framework, using relative entropy for distribution estimation.
result HS-FP model outperforms classic MVO and EW benchmarks in out-of-sample performance.
Mathai-Quillen forms are used to give an integral formula for the Lefschetz number of a smooth map of a closed manifold. Applied to the identity map, this formula reduces to the Chern-Gauss-Bonnet theorem. The formula is computed explicitly for constant curvature metrics. There is in fact a one-parameter family of inte…
New insights into MAE show it treats examples unequally and IMAE improves this.
problem Noise-robust learning in deep learning models.
method Analysis of MAE's noise-robustness and proposing IMAE to improve it.
result IMAE improves MAE's fitting ability while preserving its noise-robustness.
Proposes CGA model for logical queries over KGs.
problem Handling logical queries over incomplete KGs with unequal query path contributions.
method Multi-head graph attention with initial neighborhood aggregation for center node prediction.
result CGA model outperforms baselines on DB18, WikiGeo19, and Bio datasets.
Improved community detection in heterogeneous SBM with side information.
problem Misclassification in community detection with noisy labels.
method Optimal weighted message passing and minimum energy flow.
result Optimal weighting improves misclassification rate in heterogeneous SBM.
New findings on maximizing noise stability in partitions of Gaussian space.
problem Maximizing noise stability in partitions of Gaussian space.
method Analyzing the correlation between sets and their noise stability, proving conditional conjectures and hardness results.
result Hyperstable partitions maximize noise stability and have specific properties.
This paper addresses GE estimation in non-standard settings using various resampling methods.
problem Biased GE estimates in non-standard settings like clustered data and concept drift.
method Tailored resampling methods for clustered, spatial, unequal sampling, concept drift, and hierarchically structured outcomes.
result Standard resampling methods often yield biased GE estimates in non-standard settings.
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.