Bayesian NMF model improves predictions and avoids overfitting.
problem Predicting missing values and finding hidden patterns in nonnegative data.
method Flexible and hierarchical prior for Bayesian NMF with Gibbs sampling.
result The proposed model leads to better predictions and avoids overfitting.
Bayesian model infers factor dimensionality and sparse loading matrix adaptively.
problem Inference of high-dimensional sparse factor model with varying sparsity and factor dimensions.
method Adaptive Bayesian sparse factor model with posterior concentration.
result Posterior distribution asymptotically concentrates on true factor dimensionality and sparsity.
Bayes-Factor-VAE models improve disentanglement of latent factors in data.
problem Disentangling latent factors in data using standard Gaussian priors is suboptimal.
method Introduced hierarchical Bayesian deep auto-encoder models with hyper-priors on latent variances.
result Bayes-Factor-VAEs outperform existing methods in latent disentanglement.
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix-tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as u…
Optimizes Bayesian priors for matrix factorization without posterior inference.
problem Selecting optimal priors for Bayesian models in machine learning.
method Prior predictive distribution and virtual statistics matching user-provided or observed data statistics.
result Analytically determines hyperparameters for Poisson factorization models.
Bayesian model identifies outliers and determines tensor rank in streaming data.
problem Outliers and over-fitting in streaming tensor factorization.
method Variational Bayesian Inference for robust tensor rank determination and outlier identification.
result Model accurately identifies sparse outliers and determines tensor rank.
VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.
problem Efficiently estimating student skill levels with uncertainty for adaptive testing.
method VarFA uses variational inference to extend factor analysis models for educational data.
result VarFA efficiently handles large datasets and produces uncertainty estimates.
Deep model learns complex latent codes without assuming factor structure.
problem Learning latent codes with complex, non-factorial distributions.
method Deep generative factor analysis with beta process prior and stochastic EM algorithm.
result Preliminary results show model can approximate complex distributions.
A new particle-based method improves Bayesian NMF for better uncertainty estimates.
problem Lack of principled uncertainty understanding in traditional NMF.
method Particle-based variational approach to Bayesian NMF.
result Better particle approximations to Bayesian NMF posterior in less time.
Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to large scale models. This paper presents full Bayesian inference via VB on both single…
We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t"---known as dyadic events---in order to form an…
Bayesian model identifies cancer pathways using genomic data.
problem Identifying altered pathways associated with specific cancer types.
method Bayesian semi-nonnegative tri-matrix factorization incorporating biological prior knowledge.
result Pathways identified can be used as prognostic biomarkers.
DF2M uses deep neural networks within a factor model for high-dimensional functional time series forecasting.
problem Forecasting high-dimensional functional time series with explainability and accuracy.
method Bayesian nonparametric model based on Indian Buffet Process and multi-task Gaussian Process, incorporating a deep kernel function.
result DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models.
Automates model comparison in probabilistic programming.
problem Manual derivations for model comparison are error-prone and time-consuming.
method Message passing on a Forney-style factor graph with a custom mixture node.
result Automates Bayesian model averaging, selection, and combination.
Paper proposes VAE-BPTF for better tensor factorization of sparse, imbalanced count data.
problem Inference of Bayesian Poisson-Gamma models for sparse and imbalanced count data is challenging.
method Variational auto-encoder framework with multi-layer perceptron networks for complex update information sharing and reweighting.
result VAE-BPTF outperforms current models in reconstruction errors and latent factor coherence across real-world datasets.
Enhances FA framework for heterogeneous data with feature selection and semi-supervised learning.
problem Feature extraction and latent representation of heterogeneous data.
method Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA) framework.
result SSHIBA outperforms state-of-the-art algorithms and shows interpretability gains.
This study improves fast non-Bayesian Poisson factorization for implicit-feedback recommendation systems.
problem Improving recommendation quality and speed for implicit-feedback data.
method Regularized Poisson models, frequentist optimization, sparse solutions.
result Frequentist approach yields better top-N recommendations with shorter fitting times.
A Bayesian nonparametric approach for continual learning using neural networks.
problem Catastrophic forgetting in neural networks during sequential task settings.
method Indian Buffet Process (IBP) prior for dynamic model expansion and factorization of weight matrices.
result The method promotes positive knowledge transfer between tasks and allows for dynamic model complexity.
We propose a new IRT model that directly factors test items without factor analysis.
problem Existing multidimensional IRT methods require factorization, which is posthoc and linear.
method We use a sparsity-promoting horseshoe prior to factorize items directly within the IRT model.
result Our model performs factorization directly and consistently selects the correct number of factors.
Unified model combines shrinkage, views, and factor models for better portfolio selection.
problem Limitations of mean-variance analysis, estimation errors, and reliance on historical data.
method Bayesian approach integrating shrinkage estimation and Black-Litterman model with Fama-French factor models.
result The model outperforms simple and sample-based optimal portfolios in US equity market.
Bayesian nonparametric models for data with heterogeneous particles.
problem Deconvolving data with heterogeneous particles, like voter tallies in elections.
method Nonparametric deconvolution models (NDMs) using two tiers of Dirichlet processes.
result NDMs can recover how factor distributions vary locally for each observation.
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternati…
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the cas…
Bayesian method filters unevenly-sampled time series.
problem Bayesian nonparametric low-pass filtering for unevenly-sampled time series.
method Latent-factor model with Gaussian processes for time series, Bayesian inference.
result The proposed model identifies low-pass filtering as low-frequency latent component via Bayesian inference.
New Bayesian method for sparse multidimensional item response theory.
problem Sparse interpretable explanations for questionnaire data.
method Bayesian EM algorithm for sparse factor loadings.
result Reliable recovery of factor dimensionality and latent structure.
A Bayesian Boolean Matrix Factorization for cancer genomics
problem Identifying coordinated feature changes in cancer
method Bayesian Boolean Matrix Factorization
result Captures widespread, near-simultaneous chromosome-number changes
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model…
A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparamet…
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a regular statistical model, resulting that theoretical optimization me…
The paper automates machine learning pipelines using probabilistic matrix factorization and Bayesian optimization.
problem Automating the selection and tuning of machine learning pipelines.
method Combining collaborative filtering and Bayesian optimization with probabilistic matrix factorization.
result The approach quickly identifies high-performing pipelines across various datasets, significantly outperforming state-of-the-art methods.
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We…
Proposes a regularization method for Bayesian networks to improve model generalization.
problem Improving model generalization in Bayesian networks.
method Distribution-based penalization approach that encourages similar conditional probability distributions.
result Proposed method solves the wave propagation modeling problem better than baseline methods.
Automates Bayesian signal processing algorithm design using factor graphs.
problem Designing efficient Bayesian signal processing algorithms.
method Factor graph approach and ForneyLab tool for automated inference.
result ForneyLab outperforms competitors in automated inference for state-space models.
Evidence Networks simplify Bayesian model comparison for complex models.
problem Bayesian model comparison challenges with intractable likelihoods or priors.
method Loss functions and neural networks for fast, amortized estimation of Bayes factors.
result Evidence Networks provide accurate and scalable Bayes factor estimation.
Paper proposes new metrics to compare asset pricing models, incorporating Bayesian insights.
problem Power problems of statistical tests and misuse of alpha-based statistics.
method Unified set of distance-based performance metrics derived from alphas and standard errors, Bayesian interpretation of model performance.
result Bayesian approach favors models with low alpha dispersion and high explanatory power, especially the momentum factor.
Efficient CVI for NGFA improves GFA inference for large-scale data.
problem Inference limitations in GFA models for large-scale data.
method Collapsed variational inference for nonparametric Bayesian GFA.
result CVI algorithm effectively approximates NGFA posterior in collapsed space.
Bayesian method identifies multi-way interactions among predictors.
problem Identifying meaningful interactions among multiple variables.
method Factorization mechanism and Gibbs sampling for posterior inference.
result Posterior consistency of the regression model.
Bayesian method tests Granger causality in functional time series.
problem Testing Granger causality between functional time series.
method Bayesian dynamic linear models (DLM) and Bayes Factor.
result Captures Granger causality between yield curves and weather conditions.
SMURFF accelerates Bayesian Matrix Factorization for large datasets.
problem Efficient implementation of Bayesian Matrix Factorization for large datasets.
method High-performance framework for composing and constructing different Bayesian matrix-factorization methods.
result SMURFF enables large-scale runs of compound-activity prediction.
Bayesian Temporal Factorization predicts multidimensional time series with missing data.
problem Predicting large-scale, multidimensional spatiotemporal data with missing values.
method Integrates low-rank matrix/tensor factorization and VAR process into a probabilistic model.
result Superior performance on real-world spatiotemporal data sets compared to existing methods.
EigenBayes: A fast, adaptive Bayesian shrinkage approach for high-dimensional matrix factorization
problem Choosing the latent dimension k in factor models method Adaptive spectral shrinkage and empirical Bayes calibration
result Adapts to signal-to-noise ratio and shrinks superfluous components
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.
Relational learning can be used to augment one data source with other correlated sources of information, to improve predictive accuracy. We frame a large class of relational learning problems as matrix factorization problems, and propose a hierarchical Bayesian model. Training our Bayesian model using random-walk Metro…
New BAM model connects tensor factorization and topic models using Polya Urns.
problem Efficiently modeling and analyzing nonnegative tensors and topic distributions.
method Dynamic generative model BAM based on Poisson process and Polya-Bayes process.
result Developed efficient simulation algorithms for NTF and topic models.
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to …
A new criterion HBIC improves model selection for factor analysis with missing data.
problem Model selection for factor analysis with incomplete data.
method Proposes a novel criterion HBIC that uses actual observed information in the penalty term.
result HBIC is more accurate than BIC when missing data rates are high.
This project compares MCMC and VI for Bayesian PMF on MovieLens.
problem Intractable posterior distribution in PMF.
method Employed MCMC and VI for Bayesian inference on MovieLens.
result VI converges faster, MCMC provides more accurate estimates.