Develops generic spike-and-slab priors for high-dimensional linear regression.
problem Bayesian high-dimensional linear regression challenges.
method Proposes a class of generic spike-and-slab priors and a unified framework for theoretical assessment.
result Achieves nearly-optimal posterior contraction rate and model selection consistency under general conditions.
Spike-and-slab priors are improved for high-dimensional Bayesian regression.
problem Prohibitive computational costs for existing samplers in high-dimensional settings.
method Proposes Scalable Spike-and-Slab (S3) for high-dimensional Bayesian regression. result Improves computational cost to max{n2pt,np} per iteration, demonstrating significant speed-ups and quality gains. Paper proposes a new method for Bayesian linear regression using spike-and-slab priors.
problem Identifying predictors with similar relationships in linear regression models.
method Hierarchical Bayesian models with spike-and-slab priors and a Gibbs sampler.
result The proposed method outperforms previous methods in simulations and real data analysis.
New algorithms improve Bayesian linear regression with spike-and-slab priors.
problem Efficiently sampling from Bayesian linear regression models with sparsity-inducing priors.
method Design of two sampling algorithms: Gibbs sampling and Stochastic Localization.
result Stochastic Localization sampler shows significant advantage for poorly designed data matrices.
A new GP-LVM with spike and slab priors for latent dimension selection.
problem Selecting the number of latent variables in GP-LVM.
method Spike and slab prior for latent variables, efficient variational inference.
result The new model provides a principled approach for latent dimension selection.
New algorithms sample spike-and-slab priors efficiently in high dimensions.
problem Sampling from spike-and-slab priors in high-dimensional settings.
method Provably efficient algorithms for posterior sampling with sublinear measurement count.
result First provable algorithms for spike-and-slab posterior sampling without strong SNR assumptions.
Model for EEG source localization with spatio-temporal structure.
problem Solving MMV problems with spatio-temporal sparsity patterns.
method Generalized spike and slab prior with Expectation Propagation.
result Demonstrated viability of the proposed model and inference scheme.
A fast and scalable method for variable selection in high-dimensional Gaussian processes.
problem Inefficient variable selection in high-dimensional Gaussian processes.
method Developed a fast and scalable variational inference algorithm for spike and slab Gaussian processes.
result Consistently outperforms vanilla and sparse variational GPs while retaining similar runtimes.
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and bi…
Bayesian l0-regularized least squares for high-dimensional predictors.
problem Optimizing a non-convex objective function over model space.
method Spike-and-slab priors with single Best Replacement (SBR) for scalability.
result SBR can find the spike-and-slab estimator, bridging Bayesian regularization and proximal updating.
We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…
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.
Improved VB algorithm for high-dimensional logistic regression with theoretical guarantees.
problem Sparse high-dimensional logistic regression model selection.
method Spike and slab variational Bayes approximation.
result Optimal convergence rates in ℓ2 and prediction loss for sparse truths. Bayesian model tackles spatio-temporal underdetermined problems.
problem Solving underdetermined linear inverse problems with spatial and temporal sparsity constraints.
method Generalized spike-and-slab prior with transformed Gaussian process, expectation propagation algorithm, and approximations for scalability.
result Demonstrated effectiveness on synthetic and real data sets.
A new method discovers equations from data using Bayesian and kernel techniques.
problem Discovering equations from data is hard due to sparsity and noise.
method Kernel regression for function estimation and Bayesian spike-and-slab prior for uncertainty quantification.
result KBASS method outperforms state-of-the-art methods on benchmark tasks.
Bayesian SPCA method tackles orthogonality constraint with spike and slab prior.
problem Bayesian SPCA method for high-dimensional data with orthogonality constraint.
method Parameter-expanded coordinate ascent variational inference (PX-CAVI) with spike and slab prior.
result PX-CAVI algorithm outperforms existing SPCA approaches in performance.
The paper extends and applies a new shrinkage prior in Bayesian factor analysis.
problem Estimating the number of factors in sparse Bayesian factor analysis.
method Introduces and extends a generalized cumulative shrinkage process (CUSP) prior.
result Exchangeable spike-and-slab shrinkage priors imply increasing shrinkage as the column index increases.
Bayesian neural network achieves nearly optimal performance in Besov space.
problem Bayesian neural networks in Besov space.
method Spike-and-slab prior and shrinkage prior for posterior convergence rate.
result The posterior convergence rate is nearly minimax and adaptive to unknown smoothness.
In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, a…
We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new feature learning and extraction procedure based on a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C has not prio…
We consider the problem of using a factor model we call {\em spike-and-slab sparse coding} (S3C) to learn features for a classification task. The S3C model resembles both the spike-and-slab RBM and sparse coding. Since exact inference in this model is intractable, we derive a structured variational inference procedure …
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularis…
Paper proposes new Bayesian neural network models for efficient learning.
problem Efficient learning and model compression in deep neural networks.
method Proposes Spike-and-Slab Group Lasso (SS-GL) and Spike-and-Slab Group Horseshoe (SS-GHS) priors for structured sparsity in Bayesian neural networks.
result Establishes competitive performance in prediction accuracy, model compression, and inference latency compared to baseline models.
Proposes a method to propagate uncertainty in neural networks for sparse coding.
problem Uncertainty in neural networks for sparse coding.
method Representing the target vector as a spike and slab distribution at each layer, deriving gradients of normalisation constants, and using Bayesian inference.
result Designs a novel Bayesian neural network for sparse coding.
Wavelet-based fANOVA method improves factor analysis.
problem Efficiently analyzing functional data with multiple factors.
method Bayesian hierarchical model with spike-and-slab mixture and NIG conjugate setup, combined with Markov grove graphical model.
result Method outperforms existing wavelet-domain fANOVA methods in various settings.
Bayesian GAMs improve predictive performance for high-dimensional data.
problem Sparse regularization in GAMs leads to excess shrinkage and difficulty in selecting nonlinear effects.
method Developed a novel spike-and-slab LASSO prior and scalable EM-Coordinate Descent algorithm.
result Improved predictive and computational performance compared to existing models.
Exact inference in the linear regression model with spike and slab priors is often intractable. Expectation propagation (EP) can be used for approximate inference. However, the regular sequential form of EP (R-EP) may fail to converge in this model when the size of the training set is very small. As an alternative, we …
Proposes a flexible MGP model for dynamic, sparse correlations.
problem Handling dynamic and sparse correlations in multivariate data.
method Non-stationary MGP with dynamic spike-and-slab prior and EM algorithm.
result Captures dynamic and sparse correlations effectively.
Improved Thompson Sampling for high-dimensional sparse bandits.
problem Stochastic linear contextual bandits with high-dimensional features.
method Thompson Sampling with spike-and-slab priors and variational inference.
result Nearly optimal upper bound on expected cumulative regret.
IDS improves sparse linear bandits by balancing information and regret.
problem Sparse linear bandits in high-dimensional decision-making.
method Information-directed sampling (IDS) with Bayesian regret bounds and empirical Bayesian sparse posterior sampling.
result IDS nearly matches existing lower bounds and significantly reduces regret.
CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.
problem Inefficiency of global similarity measures in transfer learning for high-dimensional data.
method Conditional spike-and-slab prior with covariate-specific priors for robust partial information transfer.
result CONCERT achieves variable selection and information transfer simultaneously, improving performance on the target.
PliableBVS extends Bayesian lasso for modeling interactions with modifying variables.
problem Modeling interactions between large and small sets of variables, especially in omics studies.
method Bayesian variable selection with spike-and-slab priors and hierarchical structure.
result PliableBVS outperforms pliable lasso in identifying active main and interaction effects.
DABS uses a policy network to select experiments in high-dimensional design spaces.
problem Adaptive factorial screening in high-dimensional discrete design spaces.
method DABS learns a policy network offline to sequentially select experiments, incorporating sparsity and interactions via a spike-and-slab prior.
result DABS achieves superior accuracy and scalability over classical and Bayesian baselines under tight experimental budgets.
Efficiently identifies important variables in binary outcomes using variational Bayes.
problem Bayesian variable selection for binary outcomes with computational challenges.
method Mean-field variational Bayes approximation with closed-form updates and efficient inference algorithm.
result Successfully identifies important variables and is orders of magnitude faster than MCMC.
Improves variational inference for sparse models using mixtures of exponential families.
problem Intractability of posterior distributions in Bayesian sparse models.
method Flexible mean field variational inference using mixtures of non-overlapping exponential families.
result Mixtures of exponential families with non-overlapping support form an exponential family, enabling analytical updates.
Proposes a Bayesian approach for automatic node selection in sparse neural networks.
problem Reduces structural complexity and computational speedup in large-scale predictive models.
method Uses spike-and-slab Gaussian priors and variational Bayes approach for node selection.
result Establishes variational posterior consistency and optimal contraction rates for sparse networks.
Paper develops efficient variational inference for sparse deep learning with theoretical guarantees.
problem Sparse deep learning's challenge of huge storage consumption and sparse structure recovery.
method Bayesian treatment with spike-and-slab priors and continuous relaxation of Bernoulli distribution for computationally efficient variational inferences.
result Provides variational posterior contraction rate, justifying consistency of the proposed method.
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive crossvalidatio…
In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive cross-validati…
New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.
problem Sampling from distributions with discontinuous gradients.
method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.
result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.
Bayesian approach improves deep learning performance by concentrating posterior on optimal networks.
problem Improving generalizability of deep neural networks by reducing overfitting and optimizing architecture design.
method Spike-and-Slab Deep Learning (SS-DL) for fully Bayesian regularization of ReLU networks.
result Posterior distribution concentrates at near minimax rate for unknown smoothness levels, matching optimal network size.
Bayesian model predicts phenotype effects from multi-environmental factors.
problem Predict phenotype effects from multi-environmental trials.
method Bayesian tensor regression with spike-and-slab structure.
result Model outperforms previous methods in simulation and real-world data.
Study improves theoretical understanding of Bayesian deep learning for classification tasks.
problem Theoretical gap in understanding Bayesian approaches in deep learning for classification.
method PAC-Bayes bounds techniques and Spike-and-Slab priors for sparse deep learning.
result Established non-asymptotic results for prediction error, achieving minimax optimal rates.
Improves feature selection in high-dimensional data using LLM-generated weights.
problem Inaccurate LLM-generated weights degrade feature selection performance.
method Integrates LLM-generated weights into prior inclusion probabilities using LLM Sparsity Prior (LSP).
result Improves prediction accuracy and identifies clinically relevant features.
Bayesian model for sparse regression with spatial-temporal structure.
problem Sparse linear regression with spatio-temporal constraints.
method Hierarchical Gaussian process prior and Expectation Propagation algorithm.
result Model successfully applied to real data.
New AMP algorithm solves hard non-convex sparse recovery problem.
problem Sparse signal recovery with spike and slab priors.
method Greedy and adaptive matching pursuit algorithm.
result Superior cost-quality trade-off over existing alternatives.
New method estimates sparse canonical vectors efficiently.
problem Sparse canonical vectors estimation in CCA.
method Quasi-Bayesian estimation via Rayleigh quotient function.
result Achieves minimax rate with low computational cost.
Bayesian framework selects features and lags for time series forecasting.
problem Variable selection and lagged error term identification in time series models.
method Hierarchical Bayesian models with spike-and-slab priors, two-stage MCMC algorithm.
result Posterior selection consistency under mild conditions, improved predictive performance.