New test improves clustering accuracy for Gaussian mixtures.
problem Improving clustering accuracy for Gaussian mixtures.
method Relative fit test for Gaussian Mixture Models.
result New test provides provable error control and higher power.
On-device research index
arXiv research
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.
168,982 papers · 148 categories
Trend · papers per month
48 results for “mixture testing”
The paper improves Fisher-Pitman tests for Poisson mixtures, detecting autism-related genes.
problem Detecting differentially expressed genes between autism and control subjects.
method Nonparametric Poisson mixtures and Fisher-Pitman permutation tests.
result The tests reveal genes missed by common methods, demonstrating rate optimality.
New method tests mixtures of distributions with fewer samples than previously thought.
problem Testing if a distribution is a mixture of known distributions.
method Noise model where the noisy distribution is a mixture of the original and known noise.
result Sample complexity is the same as for non-mixture cases.
New sampling and identity-testing methods for mixtures of distributions that don't satisfy approximate tensorization of entropy.
problem Sampling and identity-testing for mixtures of distributions that don't satisfy approximate tensorization of entropy.
method Fast mixing of Glauber dynamics and efficient identity-testers in the coordinate-conditional sampling access model.
result Efficient identity-testers for mixtures of ATE distributions in the coordinate-conditional sampling access model.
Estimates parameters in a deviated Gaussian mixture model.
problem Testing goodness-of-fit between a known function and a mixture of experts.
method Constructs novel Voronoi-based loss functions to estimate parameters.
result Characterizes local convergence rates of parameter estimation more accurately.
Training on mixed distributions improves test performance even when components are unrelated.
problem Improving test performance with mismatched training and test distributions.
method Analyzing mixture distributions with different training and test proportions.
result Distribution shift can be beneficial, improving test performance even when components are unrelated.
Develops new e-processes and confidence sequences for Gaussian means with unknown variance.
problem Constructing valid t-tests and confidence sequences for Gaussian means with unknown variance.
method Explores generalized nonintegrable martingales and extended Ville's inequality, developing two new e-processes and confidence sequences.
result Analyzes the width of resulting confidence sequences with a polynomial dependence on error probability, proving it to be unavoidable and even better than classical fixed-sample t-tests.
AutoGMM automates Gaussian mixture modeling in Python.
problem Automatic clustering of complex data with uncertainty-aware grouping.
method Strategic initialization using an agglomerative Mahalanobis heuristic, parallelized model selection by information criteria.
result Strong out-of-the-box performance on classic benchmarks and real datasets.
The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.
problem Modeling nodes in Bayesian networks with complex distributions.
method Gaussian mixture models combined with double iteration algorithm.
result The double iteration algorithm optimizes Gaussian mixture models effectively.
Transformers converge linearly to optimal models for Gaussian mixtures classification.
problem Theoretical understanding of transformers' in-context classification.
method Gradient descent training of a single-layer transformer for Gaussian mixtures classification.
result Transformers converge linearly to globally optimal models for Gaussian mixtures classification.
AdaPT-GMM improves multiple testing power with covariates.
problem Powerful and robust multiple testing with covariates.
method Covariate-assisted Gaussian mixture model with adaptive thresholding.
result AdaPT-GMM delivers high power in various scenarios.
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are deter…
FastKCI speeds up KCI tests for causal inference on large datasets.
problem Cubic computational complexity of kernel-based conditional independence tests.
method Mixture-of-experts approach with parallel Gaussian process inference.
result Substantial computational speedups with maintained statistical power.
A new UU-test decides unimodality of datasets.
problem Deciding on the unimodality of a dataset for better data analysis.
method UU-test operates on the empirical cumulative density function (ecdf) to build a piecewise linear approximation that models the data as a Uniform Mixture Model.
result The UU-test provides a statistical model of the data in the form of a Uniform Mixture Model.
LSTM-MDNs improve risk forecasting during turbulent periods.
problem Forecasting Value-at-Risk (VaR) during volatile market conditions.
method Implemented Long Short-Term Memory mixture density networks (LSTM-MDNs) for VaR forecasting and compared them with established models.
result LSTM-MDNs outperformed benchmark models in turbulent periods but not in calm periods.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
problem Inaccurate posterior approximation in traditional VAEs.
method Recursive mixture estimation algorithm using functional gradient approach for iterative component selection.
result Significantly higher test data likelihood compared to state-of-the-art methods on benchmark datasets.
Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showe…
The paper examines risk aggregation under mixtures of marginals, finding that more homogeneous distributions lead to larger uncertainty.
problem Investigating the impact of mixing on risk aggregation uncertainty.
method Analyzes ordering relations and inequalities for aggregation sets under distribution and quantile mixtures.
result More homogeneous marginals result in larger aggregation sets, indicating greater model uncertainty.
A faster EM algorithm for unsupervised Gaussian mixture models.
problem Efficiently determining the number of components in Gaussian mixture models.
method Adaptive Anderson Acceleration (AA) for EM algorithm, with novel monotonicity control and covariance matrix preservation.
result Significantly faster convergence compared to non-accelerated EM, up to 60X in some cases.
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a h…
Paper proposes a method to improve language model performance on unknown distributions.
problem Language models trained on diverse data can perform poorly on unseen distributions.
method Distributionally robust optimization (DRO) to minimize worst-case performance over a mixture of potential test distributions.
result Topic CVaR approach reduces perplexity by 5.5 points compared to standard maximum likelihood.
Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priorsstat.ML
The study improves representation learning bounds using data-dependent Gaussian mixtures.
problem Improving generalization in representation learning.
method Established bounds using relative entropy and MDL of latent variables.
result The approach significantly improves generalization over existing methods.
Study shows mixtures of nonlinearities can improve deep learning performance.
problem Improving deep learning performance with large datasets and complex models.
method Analyzed random feature regression with features for a random weight matrix and random bias vector .
result Mixture of nonlinearities can improve both training and test errors over a single nonlinearity.
Testing MCMC codecs.SE
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, w…
Paper improves CI and CS for bounded means using betting and mixtures.
problem Estimating means of bounded random variables.
method Composite nonnegative martingales, testing by betting, method of mixtures.
result Empirically outperforms existing CI and CS methods.
New algorithm for learning mixtures with mostly uniform weights, improving on previous bounds.
problem Learning mixtures of Gaussians with uniform weights and mostly uniform component weights.
method Statistical Query (SQ) lower bound and quasi-polynomial upper bound for testing.
result Quasi-polynomial upper bound for testing mixtures with mostly uniform weights.
Study compares methods for recovering latent risk-neutral densities from option prices, finding DeepONet effective.
problem Accurately recovering latent risk-neutral densities from option prices is challenging.
method Two benchmarks and various methods (lognormal mixture, DeepONet, quote transformer) are used to compare recovery accuracy.
result DeepONet outperforms other methods in reducing error on latent density recovery.
Humans perceive the seemingly chaotic world in a structured and compositional way with the prerequisite of being able to segregate conceptual entities from the complex visual scenes. The mechanism of grouping basic visual elements of scenes into conceptual entities is termed as perceptual grouping. In this work, we pro…
We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based test statistic and study its asymptotics using nonparametric techniques. Under technical conditions, the limiting distribution is given by …
Mixture modelling involves explaining some observed evidence using a combination of probability distributions. The crux of the problem is the inference of an optimal number of mixture components and their corresponding parameters. This paper discusses unsupervised learning of mixture models using the Bayesian Minimum M…
The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in . This distance plays a fundamental role in machine learning and signal processing: It is a member of the broader class of -divergences, and it is related to …
A new 1-iteration GMM learning algorithm improves robustness and accuracy.
problem Improving robustness and accuracy in Gaussian Mixture Model learning.
method GMM expansion idea, 1-iteration learning algorithm, theoretical proof of convergence.
result Guaranteed convergence of the new algorithm regardless initial parameters.
Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionali…
The study examines the universality of Gaussian data in high-dimensional generalized linear estimation.
problem Understanding when Gaussian data suffices for high-dimensional generalized linear estimation.
method Sharp asymptotic expressions for test and training errors in high-dimensional Gaussian mixture data with labels from a single-index model.
result The universality of Gaussian data in error estimation depends on the alignment between target weights and mixture cluster means and covariances.
Understanding separation effects on parameter estimation in finite Gaussian mixtures
problem Minimum component separation impact on convergence rates in finite Gaussian mixtures
method Developing a unified geometric framework using Hellinger lower bounds and specialized moment-extraction test functions
result Separation complexity driven by spatial configuration of mixture components
A new method for density estimation using mixture discrepancy and moments.
problem Generalizing histogram statistics to higher dimensions.
method Density estimation via mixture discrepancy and moments (DSP-mix and MSP).
result DSP-mix and MSP are computationally tractable and maintain accuracy with increased speed.
MixMin finds optimal data mixtures for better model performance.
problem Finding the best data mixtures for improved model performance is challenging.
method Developed a gradient-based approach for optimizing a convex bi-level objective.
result MixMin mixtures uniformly improved model performance across various tasks.
Optimizes group testing for COVID-19 to reduce test numbers.
problem Minimizing tests for accurate infection detection.
method Bayesian approach with genetic algorithms and sub-modularity.
result Greedy-adaptive method provides theoretical guarantees.
A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Componentsstat.ML
A new method detects changes in mixture models quickly and accurately.
problem Detecting changes in mixture models with heavy-tailed components.
method Change-point methods based on robust and quick approach.
result The method is up to 500 times faster and more accurate than existing methods.
Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysiscs.LG
Paper improves gas species identification in complex mixtures using neural networks.
problem Identifying gas species in multi-gas mixtures with high accuracy.
method Multi-label neural networks with optimal thresholding for IR spectroscopy.
result Optimal thresholding improves classification performance over conventional methods.
Study shows neural networks outperform traditional methods in speaker identification.
problem Open-set speaker identification with large populations.
method Discriminative neural networks compared to Gaussian mixture models.
result Multi-class neural networks outperform traditional methods for large speaker populations.
Minimizes indecisions in selective classification to control misclassification rates.
problem Controlling misclassification rates in high-risk scenarios.
method Using indecisions to control misclassification rates, even below Bayes optimal.
result Control of misclassification rates to any user-specified level, even below Bayes optimal.
Aioli unifies language model data mixing methods and improves performance.
problem Optimizing the mixture of training data groups for language models.
method Unified optimization framework for dynamically adjusting mixture proportions.
result Aioli outperforms existing methods by up to 12.012 test perplexity points.
Diffusion models generate data with Gaussian Universality, matching linear model test errors.
problem Analyzing the performance of models trained on synthetic data generated by diffusion models.
method Investigates Gaussian Universality for data distributions generated via diffusion models, matching test errors of linear models trained on synthetic data to Gaussian Mixture models.
result The test error of a linear model trained on diffusion-generated data matches the test error of a linear model trained on Gaussian Mixture data with matching means and covariances per class.
Machine learning models are often used at test-time subject to constraints and trade-offs not present at training-time. For example, a computer vision model operating on an embedded device may need to perform real-time inference, or a translation model operating on a cell phone may wish to bound its average compute tim…
Optimizes cover parameter in Mapper algorithm for better visualization.
problem Tuning the cover parameter in Mapper algorithm to generate a ``nice'' graph.
method Optimizes cover by repeatedly splitting using statistical tests and Gaussian mixture model.
result Algorithm generates covers that retain dataset essence while being faster.
We propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for complex metrics; (ii) how to prevent type error inflation under continuous monitoring. The proposed test does not require knowledge of the underlying…
The paper uses moment matching method for pricing spread options under Lévy models.
problem Pricing spread options under Lévy models with mean-variance mixture.
method Moment matching method applied to Lévy models with mean-variance mixture.
result Obtains semi-closed form formulas for spread option prices.