Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally…
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48 results for “Expectation-Conditional Maximization”
A contaminated mixture model detects outliers in multivariate functional data.
problem Detecting abnormal sensor measurements in multivariate functional data.
method A contaminated mixture model that clusters and detects outliers without specifying outlier proportion.
result The model outperforms competitors and correctly detects abnormal behaviors in real data.
Paper develops NPG for risk-averse RL with ECRMs, proving global convergence.
problem Ensuring reliable performance in stochastic RL problems with risk-averse policies.
method Developed natural policy gradient updates for ECRMs-based RL problems, proving global optimality and iteration complexity.
result Global convergence of risk-averse NPG algorithm with ECRMs.
A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well…
Scalable method for Bayesian experimental design without integrating over posterior distributionmath.NA
Efficiently designs experiments without integrating posterior distributions.
problem Computational inefficiency in Bayesian experimental design for PDE-based models.
method Likelihood-free approach using ANN to approximate conditional expectation.
result Significant reduction in observation model evaluations.
New method explains high-dimensional sphere data with latent factors.
problem Understanding intricate dependence structure in high-dimensional sphere data.
method Exploratory factor analysis of the projected normal distribution with a fast alternating expectation profile conditional maximization algorithm.
result Uniformly excellent results on various data types, including tweets, brain imaging, and cancer gene expression.
In this paper we discuss Bayesian nonconvex penalization for sparse learning problems. We explore a nonparametric formulation for latent shrinkage parameters using subordinators which are one-dimensional Lévy processes. We particularly study a family of continuous compound Poisson subordinators and a family of discrete…
We establish some subprincipal estimates for Berezin-Toeplitz operators on symplectic compact manifolds. From this, we construct a family of subprincipal symbol maps and we prove that these maps are the only ones satisfying some expected conditions.
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical m…
Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…
Improved spatial prediction for massive datasets using SME model.
problem Efficiently estimating parameters in massive spatial datasets.
method Spatial Mixed Effects (SME) model with AECM algorithm for flexibility.
result Improved estimation without sacrificing prediction accuracy.
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification and clustering. For continuous data which we consider here in the context of regression and cluster analysis, MoE usually use normal experts, that is, expert components following the Gaussian distribution. …
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance…
Develops a tensor mixture model for high-dimensional data clustering.
problem Jointly modeling and clustering tensors in high dimensions.
method High-dimensional tensor mixture model with plausible dimension reduction assumptions. EHCMA algorithm for efficient estimation.
result The HECM algorithm converges geometrically to a neighborhood within statistical precision of the true parameter.
Mixture of Experts (MoE) is a popular framework in the fields of statistics and machine learning for modeling heterogeneity in data for regression, classification and clustering. MoE for continuous data are usually based on the normal distribution. However, it is known that for data with asymmetric behavior, heavy tail…
The thesis models financial returns using mixtures of generalized normal distributions.
problem Estimation issues in financial return analysis.
method Mixtures of generalized normal distributions (MGND), ECM/GEM algorithms, constrained mixture models (CMGND), GND-HMMs.
result Enhanced accuracy and interpretability in financial return modeling.
A new method for semi-supervised learning with missing data using GMM and margin confidence.
problem Handling missing data in semi-supervised learning with classification uncertainty.
method Explicitly models missingness mechanism, uses margin confidence and Aranda Ordaz function, develops ECM algorithm.
result Effective reduction of bias and robustness in semi-supervised learning with substantial missing labels.
Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market mi- crostructure noise and jumps. One technique for addressing both asynchronous returns and market microstructure is the Kalman-EM (KEM) algorithm. However the KEM approach assumes log-normal pr…
Deep P-Spline automates DNN structure selection for complex regression problems.
problem Challenges in selecting optimal network structures for DNNs.
method Linking neuron selection to knot placement in basis expansion techniques, introducing a difference penalty for automated knot selection.
result Deep P-Spline extends model class and forms a latent variable modeling framework with theoretical guarantees.
Double Machine Learning estimators are asymptotically inadmissible under structure-agnostic models.
problem Minimax estimators may be inadmissible under structure-agnostic models.
method Exhibit second-order (U-statistic) estimators that asymptotically dominate DML estimators.
result Double Machine Learning estimators are asymptotically inadmissible under structure-agnostic models.
Improved estimators for causal inference using cross-fitting and undersmoothing.
problem Estimating expected conditional covariance in causal inference.
method Double cross-fit doubly robust (DCDR) estimators with undersmoothing for non-smooth nuisance functions.
result DCDR estimators achieve -consistency and asymptotic normality under minimal conditions.
PROBE algorithm efficiently solves sparse high-dimensional linear regression.
problem Sparse high-dimensional linear regression models with complex parameter spaces.
method Partitioned empirical Bayes ECM algorithm for computationally efficient MAP estimation.
result PROBE algorithm provides robust and efficient coordinate-wise optimization.
In this paper we assume a multivariate risk model has been developed for a portfolio and its capital derived as a homogeneous risk measure. The Euler (or gradient) principle, then, states that the capital to be allocated to each component of the portfolio has to be calculated as an expectation conditional to a rare eve…
SSLfmm package improves semi-supervised learning by incorporating informative missingness in finite mixture models.
problem Improving semi-supervised learning with informative missingness in datasets.
method Estimates Bayes' classifier under a finite mixture model with MCAR and MAR missingness mechanisms.
result The classifier trained on partially labelled data can achieve lower misclassification rates than supervised methods.
SkewD robustly discovers causal relationships in skewed noise models.
problem Distinguishing cause from effect in skewed noise models.
method SkewD extends normal-distribution framework to skew-normal setting for reliable inference.
result SkewD remains robust under high skewness, improving reliability.
New method predicts aphasia severity with narrower uncertainty intervals.
problem Predicting aphasia severity in stroke patients using neuroimages.
method Sparse heteroscedastic Bayesian high-dimensional regression with H-PROBE algorithm.
result H-PROBE provides narrower prediction intervals for aphasia severity.
Paper improves VaR risk allocation by avoiding zero probability events.
problem Computing VaR contributions for zero probability events.
method Reformulates Euler contributions to a ratio of conditional expectations with strictly positive probability events.
result Proposed estimator outperforms standard Monte Carlo methods in bias and variance.
Bayesian DOE accelerates experimental design with improved efficiency.
problem Enhancing experimental design efficiency and reliability.
method Bayesian framework, conditional density estimation, informative data selection.
result Significantly improved computational efficiency of experimental design.
Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithmstat.ME
Efficient Bayesian LMM framework for high-dimensional longitudinal data.
problem Scalability and dependence in high-dimensional longitudinal data.
method Partitioned empirical Bayes ECM algorithm for scalable MAP estimation.
result Identification of genes and clinical factors associated with a lupus biomarker.
Proposes FOAGP for efficient orthogonal effect decomposition of black-box computer experiments.
problem Challenges in sensitivity analysis of black-box computer experiments with complex, nonlinear functional outputs.
method Functional-output orthogonal additive Gaussian process (FOAGP) with conditional orthogonality constraint.
result Demonstrates effectiveness in orthogonal effect decomposition and variance decomposition through simulations and real-world application.
New method selects causal features from diverse data types.
problem Discovering causal relationships from non-continuous data types.
method Transformation-Model (TRAM) based Invariant Causal Prediction (TRAM-ICP) with TRAM-GCM and TRAM-Wald tests.
result Improved power and type I error control for diverse response types.
Transformers for binary decisions are sensitive to evidence order, leading to unreliable outcomes.
problem Order sensitivity in Transformers for binary decisions leads to unreliable outcomes.
method Formalized an expectation-realization gap and developed QMV and EDFL bounds.
result Uniform permutation mixtures reduce dispersion and improve reliability.
A new ML-based filter improves data assimilation for nonlinear systems.
problem Improving data assimilation for nonlinear systems using ensemble methods.
method Developed a machine learning-based conditional mean filter (ML-EnCMF) integrating ANN and linear functions.
result ML-EnCMF outperforms EnKF and likelihood-based EnCMF in nonlinear systems.
Develops a deep survival model for causal inference in longitudinal studies.
problem Estimating treatment effects on time-to-event outcomes in observational studies with time-dependent covariates.
method TCS model using potential outcomes framework and ensemble of recurrent subnetworks.
result Identifies conditional average treatment effects and individual treatment effect heterogeneity over time.
A new method tests variable significance without assuming model correctness.
problem Testing variable significance in the presence of complex interactions.
method Flexible nonparametric or machine learning methods to estimate conditional mean independence.
result Achieves minimax optimal rate in nonparametric testing problem.
Investors with asymmetric information play a game to optimize their portfolios.
problem Two investors with different information levels compete in portfolio selection.
method Modelled as a Stackelberg game with entropy-regularized mean-variance objectives.
result Equilibria exist where follower's strategy depends on leader's actions.
Optimal tuning for estimating ECC in proportional asymptotics.
problem Estimating Expected Conditional Covariance (ECC) under proportional asymptotics.
method Debiased ridge regression estimators for nuisance functions, sample splitting strategies, and asymptotic variance analysis.
result Prediction-optimal tuning parameters may not minimize asymptotic variance of ECC estimator.
Study finds Hilbert square of real surfaces can be maximal even when the surface has disconnected real locus.
problem Exploring conditions for maximality of Hilbert square of real surfaces.
method Analyzing Hilbert square of maximal real surfaces and examining specific examples.
result Hilbert square can be maximal even for surfaces with disconnected real locus.
Improved best-arm identification in correlated multi-armed bandits.
problem Best-arm identification in multi-armed bandits with correlated rewards.
method Proposed C-LUCB algorithm that exploits upper bounds on conditional rewards.
result Significant reduction in sample complexity for best-arm identification.
Maximal knotless graphsmath.GT
Maximal knotless graphs have at least 74% of their vertices' edges.
problem Characterizing maximal knotless graphs and understanding their edge constraints.
method Analyzing edge maximality and constructing graphs to meet constraints.
result There exists an infinite family of maximal knotless graphs with fewer edges than previously thought.
The paper finds maximal metrics on Euclidean spaces.
problem Finding maximal elements in moduli spaces of Riemannian metrics.
method Defining a preorder on moduli space by isometry groups and identifying maximal elements.
result Constructs many examples of maximal metrics on Euclidean spaces.
Survey on geometry and topology of maximal antipodal sets.
problem Maximal antipodal sets on Riemannian manifolds.
method Comprehensive survey of existing research.
result Relation to various mathematical areas.
New bounds on maximal linkless graphs with improved edge-to-vertex ratios.
problem Finding maximal linklessly embeddable graphs with improved edge-to-vertex ratios.
method Constructing families of graphs and proving necessary and sufficient conditions for clique sums.
result Improved edge-to-vertex ratios for maximal linklessly embeddable graphs.
Study examines maximal domains of radial harmonic functions across different curvature types.
problem Understanding maximal domains of radial harmonic functions in various curvature settings.
method Analysis of harmonic spaces with positive, zero, and negative curvature.
result Characterization of maximal domains for radial harmonic functions in different curvature contexts.
We shall investigate maximal surfaces in Minkowski 3-space with singularities. Although the plane is the only complete maximal surface without singular points, there are many other complete maximal surfaces with singularities and we show that they satisfy an Osserman-type inequality.
New maximal surfaces solve Bernstein problems.
problem Bernstein problems in centroaffine geometry.
method Calabi affine maximal surfaces and orthonormal frame fields.
result Complete centroaffine extremal hypersurfaces solve all Bernstein problems.
The study finds that maximizing median returns is the only viable strategy in portfolio selection.
problem Difficulties in studying optimal portfolio strategies due to discontinuity and time inconsistency in maximizing median and quantile returns.
method Used intra-personal equilibrium approach to analyze portfolio selection under median and quantile maximization.
result Median maximization is the only viable strategy, with no investment in risky assets for other quantiles.
This paper surveys AUC maximization for big data and AI.
problem Assessing classifier performance for imbalanced data.
method Maximizing AUC score directly.
result No comprehensive survey of AUC maximization exists.