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 for fitting graphical models with latent variables using regularized conditional likelihood.
problem Graphical modeling with latent variables and confounding dependencies.
method Regularized conditional likelihood for exponential family graphical models.
result Framework applicable to broader settings without knowing latent variables' distribution.
Paper relaxes identifiability conditions for causal models with latent variables.
problem Challenges in identifying causal graphical models with latent variables.
method Proposes a double triangular graphical condition for nonparametric measurement models with binary latent variables.
result Guarantees identifiability of the entire causal graphical model under relaxed conditions.
New graph model handles cycles and latent variables.
problem Models for cycles and latent variables.
method Directed graphs with hyperedges (HEDGes), Markov properties.
result Markov properties for HEDGes are not equivalent.
Rejoinder to "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].
New algorithms for learning latent variables in graphical models.
problem Estimating the low-rank component in Gaussian graphical models.
method Fast, non-convex learning algorithms for the low-rank component.
result Our algorithms match the best possible sample complexity and achieve computational speed-ups.
New algorithm improves learning of latent-variable models.
problem Learning general latent-variable graphical models is challenging.
method Predictive Belief Propagation algorithm for general latent-variable graphical models.
result Significantly outperforms EM and spectral algorithms.
Develops a method to identify causal effects in linear models with latent variables.
problem Identifying causal effects in models with latent variables that are not independent.
method A novel graphical criterion and an integer linear program algorithm.
result Sufficient condition for identifying causal effects by rational formulas in the covariance matrix.
Estimates differences in brain connectivity graphs using latent variables.
problem Estimating differences in latent variable graphical models.
method Two-stage procedure: initialization and convergence stages using projected alternating gradient descent.
result Nonconvex procedure outperforms existing methods on synthetic and real data.
Paper introduces new cluster-based graphical models for high-dimensional data.
problem Inference for high-dimensional graphical models with many features.
method Cluster-based model with model-assisted clustering; likelihood-based estimation and inference strategies.
result Developed estimators for precision matrix of latent vector, with asymptotic central limit theorems.
A simple thresholding technique improves graph selection in neural connectivity studies.
problem Graphical model selection for functional neural connectivity in the presence of latent variables.
method Apply a hard thresholding operator to graphical Lasso, neighborhood selection, or CLIME estimators.
result Thresholded estimators outperform existing methods in graph selection consistency and empirical results.
Graphical-GAN combines Bayesian networks and GANs for structured data modeling.
problem Modeling structured data with complex dependencies.
method Integrates Bayesian networks and GANs, introduces structured recognition model, and generalizes EP algorithm.
result Successfully learns discrete and temporal structures on visual datasets.
We learn latent variable graphs in Gaussian models with unobserved variables.
problem Learning the structure of dependence between observed and unobserved variables in Gaussian graphical models.
method Proposed a convex optimization formulation based on structured matrix sparsity to estimate the complete connectivity of the graph including unobserved variables.
result The complete connectivity of the graph including unobserved variables can be estimated given the number of missing variables and their level of connectivity.
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…
New method for mixed data types in graphical models.
problem Challenges in analyzing data with mixed variable types.
method Latent Gaussian copula models with leveraged polychoric and polyserial correlations.
result Flexible and scalable methodology for mixed data types.
New method controls latent variables in graphical models to improve causal inference and prediction.
problem Uncertainty in causal relationships due to unobserved confounders.
method Iteratively derives proxies for latent variables from model residuals.
result Improves structural inference and prediction performance of causal models.
New neural network approach for optimizing latent variable models.
problem Stability issues in marginalizing Gaussian Bayesian networks.
method Developed a new graphical structure and a neural network algorithm.
result Established a duality between parameter optimization and neural network training.
Identifies causal effects in LiNGAM models with latent variables.
problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.
Paper tackles causal effect identification in sub-population with latent variables.
problem Identify causal effects in a sub-population with latent variables.
method Extend relevant graphical definitions and propose an algorithm for the s-ID problem.
result Sound algorithm for s-ID problem with latent variables.
Method infers latent factors influencing time-varying networks.
problem Inferring latent factors in evolving networks with hidden variables.
method Latent Variable Time-varying Graphical Lasso (LTGL) method.
result Accurately infers connectivity and hidden factor influence in multivariate time-series data.
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.
Graphical models are commonly used tools for modeling multivariate random variables. While there exist many convenient multivariate distributions such as Gaussian distribution for continuous data, mixed data with the presence of discrete variables or a combination of both continuous and discrete variables poses new cha…
Study reconstructs causal graph from latent variables using mixture oracles.
problem Reconstructing causal graphical model from data with latent variables.
method Reduction to mixture oracle to identify latent representations and causal structure.
result Conditions for identifying latent representations and causal model.
Paper compares two methods for inferring network structures in presence of latent confounders.
problem Inferring network structures in presence of latent confounders.
method Gaussian graphical models with latent variables (LVGGM) and PCA-based removal of confounding (PCA+GGM).
result Proposes a new method combining strengths of LVGGM and PCA+GGM, proving consistency and convergence rate.
We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. We propose two consistent and computationally efficient algorithms for learning minimal latent trees, that is, trees without any redundant hidden nodes. Unlike many existing methods, the observed …
New results connect RBMs to neural networks, improving learning efficiency.
problem Learning graphical models with latent variables is difficult.
method New connections to learning two-layer neural networks under ℓ∞ bounded input. result Improved algorithm for learning supervised RBMs.
Study identifies parameters in causal models with latent confounding.
problem Parameter identification in linear non-Gaussian causal models with latent confounding.
method Graphical criterion for necessary and sufficient identifiability of direct causal effects, with polynomial-time algorithm.
result Developed a graphical criterion for identifying direct causal effects in latent variable models with arbitrary non-linear confounding.
We consider the problem of covariance matrix estimation in the presence of latent variables. Under suitable conditions, it is possible to learn the marginal covariance matrix of the observed variables via a tractable convex program, where the concentration matrix of the observed variables is decomposed into a sparse ma…
Enhances method of moments for learning latent graphical models.
problem Non-convexity in learning latent graphical models.
method Introduces marginalization and conditioning for sequential learning.
result Enables learning of a broader class of loopy latent graphical models.
Tensor variable elimination for plated factor graphs enables exact inference in models with repeated structure.
problem Efficient inference in models with repeated structure.
method Generalized variable elimination to tensor variable elimination on plated factor graphs.
result Tractable inference for a class of plated factor graphs.
A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest. The edges of the graph reflect allowed conditional dependencies among the variables. Graphical models admit computationally convenient factorization properties and have long been a valuable tool for t…
Latent tree models are used in various fields like phylogenetics and computer vision.
problem Representing and analyzing complex data structures with latent variables.
method Graphical models defined on trees, focusing on tree metrics.
result Latent tree models encompass various well-known models and contain fundamental limits of what can be learned.
Method identifies causal relationships in data with latent variables.
problem Learning causal models from observational data with latent variables.
method Proposes a method to check causal paths and identify causal effects.
result Causal effects can be identified uniquely under certain conditions.
Bayesian model fuses diverse microbiome data types.
problem Challenges in fusing different types of microbiome data.
method Flexible multinomial-Gaussian generative model with variational EM algorithm.
result Inferred latent variables provide common dimensionality reduction and predictive posterior distribution.
SNJ recovers latent tree models from similarity matrices.
problem Reconstructing latent tree models from observed data.
method Spectral Neighbor Joining (SNJ) method.
result SNJ is consistent and requires fewer samples for accurate tree recovery.
New algorithm for learning RBMs with sparse latent variables.
problem Learning RBMs with sparse latent variables efficiently.
method Algorithm with time complexity O(n^(2^s+1)) for sparse RBMs.
result Improves learning time for RBMs with sparse latent variables.
Algorithm uncovers latent attribute graph from molecular data.
problem Learning latent representations and interpreting them for limited data.
method Perturbation experiments on latent codes of a generative autoencoder.
result Effective graphical model of latent codes and attributes.
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While …
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While …
New algorithm learns ferromagnetic RBMs efficiently.
problem Learning RBMs with latent variables is hard.
method Greedy algorithm based on influence maximization.
result Ferromagnetic RBMs can be learned efficiently.
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics. However, existing methods for parameter estimation, inference, and structure learning mainly rely on the Gaussian or…
Enhances neural processes for better context handling.
problem Real-world context sets are complex, requiring richer prior distributions.
method Introduces a graphical model for a richer prior on latent variables, enabling end-to-end optimization.
result Improves function modeling and test-time robustness with mixture and Student-t assumptions.
Extends VAEs to handle complex Bayesian network structures.
problem Handling complex dependency structures in Bayesian networks.
method Extends VAEs with graphical residual flows to model arbitrary dependency structures.
result Demonstrates improved performance on synthetic datasets.
GibbsNet improves deep graphical models by iteratively refining joint distributions.
problem Latent variable models struggle with specifying p(z) and iterative sampling is inefficient. method Adversarial iterative refinement to learn p(x,z) efficiently. result GibbsNet achieves both speed and expressiveness, learning complex p(z) and stable generation.