Semi-supervised singing voice separation using synthetic mixtures.
problem Singing voice separation with limited labeled data.
method Trains a single mapping function g on synthetic mixtures of singing and instrumental music.
result Performance comparable to fully supervised methods, better than semi-supervised alternatives.
Paper introduces CWDAE for better synthetic data generation.
problem Measuring discrepancy between generative and ground-truth distributions.
method Introduces mixture Cramer-Wold distance for joint and marginal distributional learning.
result CWDAE shows remarkable performance in generating synthetic data.
We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary c…
Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian α-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that e…
Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability d…
New method constructs synthetic treatment groups without mean exchangeability assumption.
problem Violations of mean exchangeability assumption in randomized controlled trials.
method Weighted mixture of treatment groups from source populations, minimizing conditional maximum mean discrepancy.
result Asymptotic normality of synthetic treatment group estimator established.
The paper shows how training with synthetic data can lead to model improvement, not degradation, under certain conditions.
problem Model collapse in iterative training on contaminated sources.
method Statistical analysis of iterative training on a mixture of true and synthetic data.
result Training with synthetic data can lead to model improvement, not degradation, under specific conditions.
MixTS uses a mixture prior to analyze Thompson Sampling in multi-task learning.
problem Analyzing Thompson Sampling in environments with uncertain and multi-class problems.
method Developed MixTS by incorporating a mixture prior into Thompson Sampling and using a novel proof technique for mixture distributions.
result Proved Bayes regret bounds for MixTS in linear bandits and finite-horizon reinforcement learning.
When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…
Study learns mixtures of smooth product distributions from samples.
problem Learning mixtures of non-parametric product distributions.
method Two-stage approach using identifiability properties of tensor decomposition and signal processing techniques.
result Recovery of component distributions under a smoothness condition.
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrate…
The paper develops methods to create reliable prediction sets for complex mixture models in high-dimensional data.
problem Building accurate prediction sets for high-dimensional mixture models with feature-dependent weights.
method The authors introduce a debiasing procedure and a novel interval combination strategy to construct valid prediction sets.
result The proposed method provides reliable coverage guarantees for prediction sets in high-dimensional mixture models.
This work addresses identifiability of nonlinear mixtures and proposes a practical algorithm.
problem Identifiability of nonlinear mixtures under unknown nonlinear distortions.
method Identification criterion based on neural network and effective learning algorithm.
result Proposes a practical method for identifying nonlinear mixtures with identifiability guarantees.
Discover causal structure from mixtures of DAGs using latent variable algorithms.
problem Discover causal structure from distributions arising from mixtures of DAGs.
method Causal structure discovery algorithms such as FCI for latent variables.
result Recover a 'union' of the component DAGs and identify varying conditional distributions.
A new method for fast Bayesian mixture model estimation.
problem Estimating Bayesian mixture models is computationally challenging.
method Amortized Bayesian Inference (ABI) framework for mixture models.
result The method provides fast inference for mixture models.
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems, and introduce a feasible approach in which parameter and structure sea…
Although Recurrent Neural Network (RNN) has been a powerful tool for modeling sequential data, its performance is inadequate when processing sequences with multiple patterns. In this paper, we address this challenge by introducing a novel mixture layer and constructing an adaptive RNN. The mixture layer augmented RNN (…
A Gaussian mixture model improves generalization for long-tailed data.
problem Optimizing generalization for rare data in long-tailed distributions.
method Suggested Gaussian mixture model and comparison of linear vs. nonlinear classifiers.
result Nonlinear classifiers outperform linear ones for long-tailed data.
New method reduces mixture model evaluation cost for large models.
problem Computational infeasibility of evaluating all mixture components.
method Combining EM and Metropolis-Hastings for stochastic sampling.
result Significantly reduced computational cost for large models.
Flexible model captures varying scales in data clusters.
problem Real-world data often exhibits varying scales or intensities, violating the homogeneity assumption of classical Gaussian mixture models.
method Individual-heterogeneous sub-Gaussian mixture model with an efficient spectral method for exact recovery.
result The method provably achieves exact recovery of true cluster labels under mild separation conditions.
Recent theoretical work has identified random projection as a promising dimensionality reduction technique for learning mixtures of Gausians. Here we summarize these results and illustrate them by a wide variety of experiments on synthetic and real data.
GGMPs improve non-Gaussian conditional density estimation.
problem Multimodality, heteroscedasticity, and strong non-Gaussianity in conditional density estimation.
method GGMP combines local Gaussian mixture fitting, cross-input component alignment, and per-component heteroscedastic GP training.
result GGMPs improve distributional approximation on synthetic and real-world datasets.
Private training and synthetic data generation using DP clustering.
problem Protecting sensitive data in deep neural networks training.
method Approximate input dataset with privately generated synthetic dataset using DP clustering.
result Simple two-layer neural network achieves SOTA classification accuracy on standard benchmark datasets.
A new model predicts multivariate regression using similarities to data points.
problem Complex, high-dimensional input-output relationships.
method Bayesian mixture-of-experts with conditional Gaussian mixtures and variational Bayes.
result Outperforms competitors in high-dimensional settings.
Hydra boosts efficiency for long-context reasoning in resource-constrained settings.
problem Quadratic complexity of transformers limits long-context reasoning in resource-constrained systems.
method Hydra uses a modular architecture with adaptive routing between sparse global attention, mixture-of-experts, and dual memories.
result Hydra achieves significant throughput and accuracy improvements for long-context reasoning.
The study characterizes learning Gaussian mixtures using GLMs in high dimensions.
problem Learning Gaussian mixtures with generalised linear models in high-dimensional settings.
method Empirical risk minimization with convex loss and regularisation.
result Exact asymptotics of the ERM estimator for Gaussian mixtures in high dimensions.
Proposes EMG mixture model for spectroscopy data.
problem Modeling residuals in spectroscopy data with positive support.
method Exponentially-modified Gaussian mixture (EMG) model with expectation-maximization algorithm.
result EMG mixture outperforms existing models in spectroscopy applications.
Generative model captures hubs and dense communities in social networks.
problem Capturing both hubs and dense communities in social networks.
method Graphon mixture model with a new condition on sparse graphs.
result Estimation of hub normalized degree and graphon for sparse components.
Dual-view mixture models cluster users with features and latent behaviors inferred from actions.
problem Clustering users based on features and latent behavioral functions inferred from indirect observations.
method Dual-view mixture models with non-parametric Dirichlet Process for automatic cluster number inference.
result Dual-view models outperform single-view models when one view lacks information.
A new method for fitting mixture models using Boltzmann exploration.
problem Challenges in learning mixture models, especially with good initialisation.
method Boltzmann exploration expectation-maximisation (BEEM) algorithm.
result BEEM can escape local optima and is insensitive to parameter initialisation.
Bayesian framework learns latent preference archetypes for many-objective optimization.
problem Expanding space of trade-offs and context-dependent human values.
method Dirichlet-process mixture model for latent preference archetypes, hybrid queries for efficient information.
result Mixture-aware Bayesian optimization outperforms standard methods on synthetic and real-world benchmarks.
Langevin Dynamics fails to sample from mixture distributions efficiently.
problem Analyzing Langevin Dynamics for sampling from mixture distributions.
method Theoretical analysis of Langevin Dynamics and proposing Chained-Langevin Dynamics.
result Langevin Dynamics fails to sample from mixture distributions efficiently.
End-to-end learnable Gaussian mixture priors improve diffusion models' exploration and expressiveness.
problem Challenges in diffusion models when priors differ from target distributions.
method End-to-end learnable Gaussian mixture priors (GMPs) with iterative refinement.
result Significant performance improvements across various benchmark problems.
Mixes higher-order simplicial complexes for data augmentation.
problem Lack of labeled data for complex systems with multiway interactions.
method Proposes mixup mechanisms for simplicial complexes, including linear and nonlinear mixup, and a convex clustering mixup.
result Synthetic simplicial complexes interpolate between existing data based on homomorphism densities.
Multi-output Gaussian processes (MOGPs) are an extension of Gaussian Processes (GPs) for predicting multiple output variables (also called channels, tasks) simultaneously. In this paper we use the convolution theorem to design a new kernel for MOGPs, by modeling cross channel dependencies through cross convolution of t…
Proposes a robust FMR model for handling sample heterogeneity.
problem Handling sample heterogeneity with a single regression model.
method Clusters samples and jointly models multiple incomplete mixed-type targets.
result Achieves state-of-the-art performance on synthetic and real-world data.
New EM algorithm for mixtures of elliptical distributions handles missing data and outliers.
problem Missing data imputation for noisy and non-Gaussian data.
method Investigation of a new EM algorithm for mixtures of elliptical distributions.
result The proposed algorithm is robust to outliers and competitive with other methods.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
Differentially private method for synthetic data generation from vertically partitioned data.
problem Generating synthetic data from vertically partitioned data while preserving privacy.
method Differentially private stochastic gradient descent (DP-SGD) algorithm combined with secure multiparty computation (MPC).
result Comparable accuracy to non-partitioned data, demonstrating privacy-preserving synthetic data generation.
Optimal classifiers derived from GMMs are approximated by deep neural networks.
problem Binary classification of high-dimensional overlapping Gaussian mixtures.
method Closed-form expressions for Bayes optimal decision boundaries derived from GMMs' eigenstructure. Empirical validation through synthetic and real-world data.
result Deep neural networks approximate optimal classifiers for GMMs, with decision thresholds related to covariance eigenvectors.
Algorithm constructs coresets for clustering time series data from Gaussian mixtures.
problem Clustering time series data from Gaussian mixtures with autocorrelations.
method Developed an efficient algorithm to construct coresets for the maximum likelihood objective.
result Size of the coreset is independent of N and k, polynomial in d, k, and 1/ε. A new model improves clustering by reducing redundancy in mixture of local EPCAs.
problem Redundancy in traditional mixture models causes ambiguity in clustering.
method Introduced a repulsiveness-encouraging prior and a DPP for diversity in DEPCAM model.
result The method reduces model redundancy and improves clustering performance.
Paper introduces MPPGA for integrating multiple PGA models on Riemannian manifolds.
problem Challenges in dimensionality reduction on Riemannian manifolds with multiple modalities.
method Develops a mixture probabilistic principal geodesic analysis (MPPGA) model.
result Demonstrates improved clustering and shape analysis using MPPGA.
A new law limits kurtosis contrast in balanced mixtures.
problem Kurtosis-based ICA fails in wide, balanced mixtures.
method Proved a redundancy law and showed purification restores contrast.
result Kurtosis contrast obeys O(κmax/Reff) in balanced mixtures. We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarc…
This paper models time-series data with a mixture of Markov chains, automatically determining the number of components.
problem Tackles the inability of common Markov state modeling frameworks to discern heterogeneities in complex data.
method Uses a mixture of Markov chains and variational expectation-maximization algorithm for automatic component selection.
result Achieves performance consistent with theoretically optimal error scaling, identifying meaningful heterogeneities in various data sets.
New method identifies shared components from unpaired multimodal mixtures.
problem Identify shared components from unpaired multimodal mixtures.
method Distribution divergence minimization-based loss with sufficient conditions for identifiability.
result Sufficient conditions for shared component identifiability from unaligned multimodal mixtures.
Method estimates joint probability density from samples using low-rank decomposition and random projections.
problem Estimating joint probability density from limited samples.
method Low-rank tensor decomposition, dictionaries, and Radon transforms.
result Algorithm outperforms previous methods in estimating synthetic probability densities.