New method aggregates nodes in sparse graphical models.
problem Estimating edge-sparse graphical models.
method Tree-aggregated graphical lasso (tag-lasso) method.
result Aggregates nodes in a data-driven fashion using a tree.
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48 results for “Sparse Graphical Models”
New method guards against outliers in sparse GGMs estimation.
problem Outliers in high-dimensional data affect robust estimation of GGMs.
method Trimmed Graphical Lasso for robust sparse GGMs estimation.
result Our method provides statistical guarantees and outperforms existing approaches.
Bayesian method selects sparse models efficiently with less bias.
problem Sparse model selection and regularization in Gaussian graphical models.
method Continuous spike-and-slab framework with EM algorithm for fast explorations.
result Efficient selection of sparse models with less bias compared to other methods.
We find a closed-form determinant for a specific sparse covariance matrix model.
problem Finding the determinant of a specific class of sparse positive definite matrices.
method Using Fourier transform of local factors, Normal Factor Graph Duality Theorem, and Matrix Determinant Lemma.
result We derive a closed-form expression for the determinant.
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Modelsstat.ML
Paper identifies sparse structures and communities in heterogeneous graphical models.
problem Detecting community structures in graphical models.
method Novel decomposition into sparse and low-rank parts, three-stage estimation procedure.
result Consistent model selection for adaptive penalized estimator.
Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. I…
The paper improves Bayesian precision matrix estimation for high-dimensional sparse data.
problem Estimating sparse precision matrices in high-dimensional settings.
method Tempered posterior with fully specified horseshoe prior.
result Concentration results and theoretical oracle inequality for posterior.
New algorithm speeds up LVGGM estimation by solving nonconvex optimization.
problem Estimating the latent variable Gaussian graphical model with sparse and low-rank components.
method Sparsity constrained maximum likelihood estimator with alternating gradient descent and hard thresholding.
result Our algorithm converges linearly to the optimal components up to statistical precision.
Proposes a method to estimate sparse Gaussian graphical models with hidden clustering structure.
problem Modeling statistical relationships between variables with sparsity and clustering.
method Two-phase algorithm using sGS-ADMM for initial point and pALM for solution.
result Demonstrates good performance and efficiency of the proposed model and algorithm on synthetic and real data.
New methods improve inference for sparse, undirected models.
problem Inference for sparse, undirected models is challenging due to intractable partition functions.
method Persistent VI for variational inference and Fadeout for reparameterization under sparsity-inducing priors.
result Improved learning of sparse undirected graphical models in simulations and real-world problems.
Efficient algorithms solve joint graphical lasso problems.
problem Learning graphical models from sparse data.
method Proximal gradient procedures with ADMM backtracking option.
result Proposed algorithms achieve high accuracy and precision.
Estimates sparse Gaussian graphical models using discrete optimization.
problem Learning a sparse graph from Gaussian graphical models.
method Proposes GraphL0BnB, an -penalized MIP solved with a custom BnB framework.
result Significant runtime and statistical performance improvements over existing methods.
One of the fundamental tasks of science is to find explainable relationships between observed phenomena. One approach to this task that has received attention in recent years is based on probabilistic graphical modelling with sparsity constraints on model structures. In this paper, we describe two new approaches to Bay…
Efficient optimization for large-scale conditional Gaussian graphical models.
problem Scalable optimization for L1-regularized conditional Gaussian graphical models.
method Proposes a new Newton method to iteratively solve two sub-problems, extending to large problems with block coordinate descent.
result Significant improvement in computation time and scalability to one million dimensional problems.
BPASGM uses sparse graphical models to optimize portfolio selection.
problem Portfolio optimization in high-dimensional settings with estimation error.
method BPASGM extends BPA to a sparse graphical model, screening assets for diversification.
result BPASGM portfolios outperform standard mean-variance portfolios in risk-adjusted performance.
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structurecs.LG
DIFFEE estimates changes in Gaussian Graphical Models efficiently and scalably.
problem Estimating changes in dependency structures of Gaussian Graphical Models.
method DIFFEE: a novel method for sparse change estimation in high-dimensional GGMs using an elementary estimator.
result DIFFEE achieves asymptotic convergence rates similar to state-of-the-art estimators but with faster computation.
New algorithm detects changes in high-dimensional networks efficiently.
problem Detecting abrupt changes in sparse Gaussian graphical models.
method Online algorithm based on monitoring conditional log-likelihood.
result Good performance across various experimental settings.
Introduces a new model for directed relationships in Gaussian data.
problem Learning directed relationships in Gaussian data.
method Developed a new directed graphical model (GGIM) from Gaussian data, leveraging stationary Gaussian processes on graphs.
result GGIMs can be framed as a LASSO problem and have a bound on the difference from the -norm penalized maximum log-likelihood estimate.
Graphical lasso and CLIME methods fail for sparse models with nearly linear dependencies.
problem Consistency of -penalised methods for sparse precision matrix estimation.
method Various -penalised estimation methods like graphical lasso and CLIME.
result All -based methods fail dramatically for models with nearly linear dependencies.
Estimates dynamic graph structure and changepoints in multivariate time series.
problem Estimating dynamic conditional dependency structure of multivariate time series.
method Group-fused graphical lasso for piecewise constant Gaussian graphical models.
result Efficient algorithm for estimating structure and changepoints.
New method learns graphical models with latent variables for extreme events.
problem Learning graphical models with latent variables for multivariate extremes.
method Tractable convex program exttt{eglatent} for Hüsler-Reiss models.
result Consistently recovers conditional graph and latent variables.
New method infers graph from dependent matrix data.
problem Inferring graph from dependent matrix data.
method Sparse-group lasso-based frequency-domain formulation with ADMM approach.
result Local convergence of inverse PSD estimators to true value.
ISEE method efficiently estimates large precision matrices in Gaussian graphical models.
problem Estimating large precision matrices in ultra-large Gaussian graphical models.
method ISEE method combines sparse modeling and large covariance matrix estimation.
result ISEE method can recover graphical structure with significant probability and efficient estimation of link strengths.
New algorithms learn GGMs without condition number bounds, even with strong dependencies.
problem Learning Gaussian Graphical Models without condition number bounds.
method Polynomial-time algorithms for attractive and walk-summable GGMs.
result Efficient recovery of graph structure with logarithmic number of samples.
NGRs merge sparse graph recovery with PGMs for efficient probabilistic inference.
problem Efficiently recover sparse graphs and learn distributions over variables.
method Integrates sparse graph recovery methods with PGMs using Graph-constrained path norm.
result NGRs can handle multimodal data and perform sparse graph recovery and probabilistic inference.
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Modelsstat.ML
FASJEM fast and scalable estimates multiple related sparse Gaussian Graphical Models.
problem Jointly estimating multiple sparse Gaussian Graphical Models for many related tasks under high-dimensional data.
method FASJEM uses an entry-wise approach with a proximal algorithm to optimize the model, achieving a consistent estimation with a convergence rate of O(log(Kp)/n_tot).
result FASJEM shows significant improvements in accuracy, computational complexity, and memory costs over baselines.
A new method for sparse regression models using graph structure.
problem Sparse regression models for high-dimensional data.
method Decomposes coefficient vector into latent variables, performs regularization on latent variables, uses proximal projection.
result Stable performance compared to other models, especially for high-dimensional data.
We learn sparse precision matrices from compressed data sketches.
problem Learning a graph from high-dimensional data with limited storage.
method Estimate a sparse precision matrix from a sketch of the data using non-linear random features.
result It is possible to estimate a sparse precision matrix from a sketch of size $m=Ω\left((d+2k)\log(d)
ight)$.
Method compares Gaussian Graphical Models to identify differences in brain connectivity.
problem Comparing functional connectivity between two populations.
method Debiased multi-task fused lasso to characterize parameter differences.
result Confidence intervals on edge differences in Gaussian Graphical Models.
Develops robust image classification models using probabilistic graphical models.
problem Robust image classification under acquisition noise and insufficient training data.
method Discriminative learning framework exploiting multiple projections and conditional correlations.
result Robust graphical model classifier minimizes classification error.
The -norm fails to produce sparse solutions in Laplacian constrained graphical models, leading to a complete graph.
problem Learning a sparse graph under Laplacian constrained Gaussian graphical models.
method Introduced a nonconvex sparsity penalty and proposed a new estimator using a sequence of weighted -norm penalized sub-problems. Developed a projected gradient descent algorithm with linear convergence rate.
result The proposed estimator can recover the edges correctly with high probability and is effective on both synthetic and real-world data sets.
SIMULE learns multiple sparse UGMs from aggregated data, identifying context-specific and shared interactions.
problem Jointly estimating multiple sparse UGMs from aggregated samples across different contexts.
method Constrained L1 minimization approach for multi-UGM learning.
result SIMULE achieves consistent results at rate O(log(Kp)/n_{tot}) and significantly improves over state-of-the-art methods.
The paper efficiently estimates parameters from truncated Gaussian and linear models.
problem Estimating parameters from truncated Gaussian and linear models.
method Minimizes finite population negative log-likelihood function with an l1-regularization term.
result Efficient estimation of parameters from truncated samples.
We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already essentially nonparametric, since the cliques can take only a finite number of values. Continuous data are different. The Gaussian graphical mode…
Delta-AI speeds up inference in sparse PGMs by local credit assignment.
problem Efficient inference in sparse probabilistic graphical models.
method Local credit assignment in agent's policy learning objective.
result Trained sampler recovers marginals and conditional distributions.
New method for inferring time series graph from sparse-group log-sum penalty.
problem Inferring conditional independence graph from high-dimensional stationary multivariate Gaussian time series.
method Sparse-group log-sum penalty (LSP) and alternating direction method of multipliers (ADMM) for iterative optimization.
result Local convergence of inverse PSD estimators to the true value with rate of convergence.
Sparse graph learning for dependent time series using ADMM.
problem Inferring conditional independence graph of sparse, high-dimensional stationary multivariate Gaussian time series.
method Sparse-group lasso-based frequency-domain formulation and alternating direction method of multipliers (ADMM) optimization.
result Convergence of inverse PSD estimators to true value under certain conditions.
Efficiently recovers Nash equilibria from noisy joint actions.
problem Exact recovery of Nash equilibria in sparse graphical games.
method An -regularized logistic regression algorithm.
result Logarithmic sample complexity for exact PSNE recovery.
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bickel et al.…
New algorithm identifies sparse dependencies in non-Gaussian data.
problem Learning sparse probabilistic graphical models in non-Gaussian settings.
method Algorithm based on transport maps and sparsity of graphs.
result Accurately estimates sparse Markov structure of non-Gaussian distributions.
Sparse logistic regression recovers any discrete pairwise graph model.
problem Recovering the Markov graph of discrete pairwise graphical models.
method Maximum conditional log-likelihood with convex optimization.
result The algorithm can recover any arbitrary discrete pairwise graphical model.
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completionstat.ML
Efficiently solves large-scale sparse covariance estimation problems.
problem Sparse inverse covariance estimation for large datasets.
method Thresholding the sample covariance matrix and solving a maximum determinant matrix completion problem using a Newton-CG algorithm.
result The algorithm converges to an ε-accurate solution in O(nlog(1/ε)) time and O(n) memory.
A fast method for sparse graph model recovery with few samples.
problem Sparse model selection in highly under-sampled regimes.
method Decides bond presence by considering pairs of variables one at a time, using posterior probability and Jeffreys prior.
result Comparable accuracy to best existing algorithms, more accurate with hidden variables.
Scalable Inference of Sparsely-changing Markov Random Fields with Strong Statistical Guaranteescs.LG
Efficiently infers time-varying sparse MRFs with strong statistical guarantees.
problem Inference of time-varying sparse MRFs with strong statistical guarantees.
method Constrained optimization with exact regularization, near-linear time and memory complexity.
result Sharp statistical guarantees for sparsely-changing Gaussian MRFs with as few as one sample per time.
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.
New method for disentangling latent factors with sparse dependencies.
problem Disentangling latent factors from observed variables and past factors.
method Mechanism sparsity regularization and sparse causal graphical model.
result Identifiability of latent factors up to a sparse causal graph.
JEEK improves sGGM estimation by integrating domain knowledge efficiently.
problem Estimating sparse Gaussian graphical models from aggregated samples with existing knowledge.
method Designing a novel hybrid norm to enforce shared and task-specific sparsity constraints, solved with a fast parallelizable algorithm.
result Achieves state-of-the-art prediction accuracy while significantly improving computational efficiency.
We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable corresponding to the mixture components is hidden and each mixture component over the observed variables can have a potentially different Markov graph structure and parameters. We propose a novel approach for estimati…