Variational Inference shows promise for Bayesian GARCH model estimation.
problem Bayesian estimation of GARCH-family models using Monte Carlo sampling.
method Variational Inference as an alternative to Monte Carlo sampling.
result Variational Inference is a reliable and competitive method for Bayesian learning in GARCH-like models.
New algorithms accelerate SVGD convergence using deep unfolding.
problem Improving the speed of SVGD convergence.
method Integrating deep unfolding into SVGD for parameter learning.
result Proposed algorithms achieve faster convergence in various tasks.
Improved uncertainty estimation in neural networks with VBLL.
problem Improving uncertainty estimation in neural networks.
method Deterministic variational formulation for training Bayesian last layer neural networks.
result Improves predictive accuracy, calibration, and out-of-distribution detection.
A new upper bound for variational inference improves the efficiency of Bayesian deep learning.
problem Improving variational inference in Bayesian deep learning.
method Presented a new upper bound (EUBO) for evidence, derived from KL-divergence and log marginal likelihood, and used SGD for optimization.
result The new upper bound (EUBO) is tighter than previous methods and outperforms state-of-the-art results in Bayesian neural networks.
Advances variational Bayesian neural networks using singular learning theory.
problem Discrepancies between predictive performance and variational objective in BNNs.
method Corrected asymptotic form of singular posterior distributions to inform variational family design.
result Improvements in variational free energy and generalization error with proposed normalizing flow.
Unified theory linking Bayesian and ensemble methods in deep learning.
problem Uncertainty quantification in deep learning.
method Reformulating optimisation as convex optimisation in probability measures, studying Wasserstein gradient flows.
result Unified theory explaining success of deep ensembles over variational inference.
Variational Prediction simplifies Bayesian inference without test time costs.
problem Bayesian inference's computational costs and posterior predictive distribution marginalization.
method Variational Prediction learns a variational approximation to the posterior predictive distribution using a variational bound.
result Directly learns a variational approximation to the posterior predictive distribution without test time marginalization costs.
AutoBayes automates Bayesian graph exploration for robust machine learning.
problem Learning representations invariant to nuisance variations in machine learning.
method Automated Bayesian inference framework exploring different graphical models.
result Significant performance improvement with nuisance-invariant machine learning pipelines.
QBVI uses natural gradients for efficient Bayesian learning.
problem Efficient Bayesian learning in complex models.
method Natural gradient updates in a black-box framework for exponential-family distributions.
result QBVI framework is effective for a wide range of Bayesian inference problems.
Automates VI divergence selection for efficient few-shot learning.
problem Efficiently selecting divergence measures for VI to improve performance.
method Meta-learning algorithm to learn optimal divergence metric and variational parameter initialization.
result Meta-learning approach outperforms standard VI methods across various tasks.
NVGD uses neural networks to infer distributions without kernel choices.
problem Challenges in choosing kernel functions for SVGD.
method NVGD parameterizes the witness function of the Stein discrepancy with a neural network.
result NVGD achieves good performance on various inference problems.
Review of priors in Bayesian deep learning models.
problem The importance of prior choices in Bayesian deep learning models.
method Overview of different priors and methods of learning priors from data.
result Motivate practitioners to think carefully about prior specification.
Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model uncertainty. Variational methods have been used for approximating intractable integrals t…
A new method for privacy-preserving Bayesian learning in federated learning.
problem Privacy-preserving learning of models from distributed sensitive data.
method Differentially private partitioned variational inference (DPVI) for federated learning.
result First general framework for federated Bayesian learning with differential privacy.
Bayes-CATSI uses variational Bayesian deep learning for medical time series data imputation.
problem Missing values in medical time series data.
method Bayes-CATSI integrates variational inference for uncertainty quantification and context-aware imputation.
result Bayes-CATSI outperforms CATSI by 9.57% in imputation performance.
Study analyzes EU in variational inference for Bayesian deep learning.
problem Analyzing epistemic uncertainty in variational inference for Bayesian deep learning.
method Theoretical analysis and derivation of relations between generalization error and EU measurements. Proposing a new objective function for VI.
result Proposed new objective function significantly improves EU evaluation over existing VI methods.
This paper develops efficient federated learning and unlearning methods in Bayesian models.
problem Managing epistemic uncertainty and legal right to be forgotten in decentralized networks.
method Develops federated variational inference solutions based on decentralized local free energy minimization.
result Demonstrates efficient unlearning mechanisms in federated learning and unlearning.
Bayesian algorithm improves word representations using semantic taxonomy.
problem Improving word representations in semantic taxonomy.
method Bayesian Hierarchical Words Representation (BHWR) learning algorithm combining Variational Bayes and semantic taxonomy modeling.
result BHWR produces better representations for rare words.
Variational Laplace improves Bayesian neural networks performance.
problem Improving Bayesian neural networks performance.
method Develops variational Laplace for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms other inference methods.
Bayesian priors and penalties are equivalent in variational inference.
problem Understanding the relationship between Bayesian priors and penalties in variational inference.
method Characterizing the regularizers that can arise in variational inference and providing a systematic way to compute the prior corresponding to a given penalty.
result Equivalence between Bayesian priors and penalties in variational inference.
Paper introduces VBG for Bayesian causal structure and mechanism learning.
problem Bayesian causal structure learning with uncertainty over models.
method Variational Bayes-DAG-GFlowNet (VBG) method.
result VBG outperforms existing methods in modeling posterior over DAGs and mechanisms.
Bayesian meta-reinforcement learning improves over point estimates with Laplace approximation.
problem Improving meta-reinforcement learning by providing full posterior distributions.
method Augmenting point estimates with Laplace approximation for full posterior distributions.
result Our method performs similarly to variational baselines with fewer parameters.
This thesis disentangles Gauss-Newton and variational approximations in Bayesian deep learning.
problem Understanding the interplay between the Gauss-Newton method and variational approximations in Bayesian deep learning.
method Analysis of the Gauss-Newton method and Laplace/Gaussian variational approximations for neural networks.
result The combination of the Gauss-Newton method with approximate inference can be cast as inference in a linear or Gaussian process model.
Variational Laplace improves Bayesian neural network performance without sampling.
problem Improving Bayesian neural network performance and calibration.
method Develops a new variational Laplace method for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms standard VI methods in test performance and calibration.
BayesAdapter turns pre-trained NNs into reliable BNNs with minimal overhead.
problem Scalability, accessibility, and reliability of Bayesian neural networks.
method Bayesian fine-tuning of pre-trained deterministic NNs to variational BNNs.
result BayesAdapter produces more reliable posteriors with less training overhead.
Improves Bayesian neural networks inference efficiency and accuracy.
problem Inflexibility of factorized structure in Dropout posterior.
method Introduces Variational Structured Dropout (VSD) with orthogonal transformation.
result VSD induces adaptive regularization and better generalization.
CoSMIC extends flow-based SVI to transdimensional problems.
problem Bayesian structure learning and model selection with multi-model parameter spaces.
method Normalizing flows with a combined stochastic variational transdimensional inference approach.
result Improved performance on high-cardinality model spaces.
New federated learning method for structured models.
problem Limited federated learning for structured probabilistic models.
method Structured variational inference adapted for federated learning.
result Effective algorithms compared to hierarchical Bayesian models.
Bayesian neural networks ignore data in infinite units limit.
problem Pathological behavior of posterior in over-parameterized networks.
method Mean-field variational inference in infinite hidden units limit.
result Posterior mean converges to zero, ignoring data.
Bayesian deep learning improves maintenance planning uncertainty quantification.
problem Estimating the remaining useful life of physical systems with uncertainty quantification.
method Stein variational gradient descent for training Bayesian neural networks.
result Bayesian deep learning models trained via Stein variational gradient descent outperform other methods in convergence speed and predictive performance.
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.
Study uses machine learning to solve photoacoustic tomography's inverse problem.
problem Solving the full inverse problem in photoacoustic tomography.
method Developed an approach using variational autoencoders for Bayesian estimation of the posterior distribution.
result Evaluated the approach with numerical simulations and compared it to a Bayesian solution.
New analysis shows Bayesian model averaging is suboptimal under misspecification.
problem Generalization performance of Bayesian model averaging under model misspecification.
method Novel second-order PAC-Bayes bounds to analyze generalization performance.
result New Bayesian-like algorithms with better generalization performance.
PIVID infers DAG structures from data using variational inference and permutations.
problem Estimating the structure of Bayesian networks from observational data.
method PIVID uses variational inference and continuous relaxations of discrete distributions to infer a distribution over permutations and DAGs.
result PIVID outperforms deterministic and Bayesian approaches in estimating DAG structures from data.
A new method learns posterior and predictive distributions together, reducing computational cost.
problem Sequential two-stage Bayesian inference is computationally expensive.
method Amortized variational inference targeting posterior-predictive distribution.
result Efficient online inference with more accurate predictive distributions.
Improved variational inference for logistic regression and classification.
problem Intractability of Evidence Lower Bound in variational logistic regression.
method Introducing a new bound for the expectation of softplus function, applied to variational logistic regression and Gaussian process classification.
result The new bound results in a tighter posterior and faster computation compared to Monte-Carlo methods.
vsOED optimizes experiment design with reinforcement learning for Bayesian models.
problem Optimizing the sequence of experiments in Bayesian models for efficient data collection.
method Reinforcement learning with variational posterior approximations to optimize design policy.
result vsOED achieves superior sample efficiency compared to existing methods.
New framework for variational coresets simplifies Bayesian inference for complex models.
problem Efficient Bayesian inference for complex models like neural networks.
method Black-box variational inference for coresets that handle intractable posterior distributions.
result Principled application of variational coresets to Bayesian neural networks.
Federated learning for Bayesian clustering of large datasets.
problem Bayesian model-based clustering of large-scale binary and categorical data.
method Federated variational inference with local merge and delete moves in parallel batches, followed by global merge moves.
result Empirical validation shows superior performance compared to existing algorithms.
The paper develops scalable variational inference for Bayesian neural networks under model and parameter uncertainty.
problem Combining structural and parameter uncertainties in scalable Bayesian neural networks.
method Adapted variational inference with reparametrization for model space constraints.
result Comparable accuracy with sparse inference compared to ordinary BNNs.
PVI seeks a posterior that makes predictions closer to true data, not approximating the Bayesian posterior.
problem Finding meaningful posterior distributions under model misspecification.
method Predictive variational inference (PVI) seeks an optimal posterior density for close predictive matching to true data.
result PVI learns a posterior that is not the same as the Bayesian posterior, but is closer to the true data generating process.
DSVGD improves federated learning with fewer communication rounds.
problem Federated learning scalability and trustworthiness.
method Distributed Stein Variational Gradient Descent (DSVGD) for non-parametric Bayesian inference.
result DSVGD achieves comparable accuracy and scalability to other methods, with well-calibrated predictions.
Bayesian neural networks improve deep learning's accuracy and uncertainty estimation.
problem Overconfident predictions, adversarial attacks, and variability underestimation in deep models.
method Stochastic relaxation of feed-forward rectified neural networks with sparsity-promoting priors and Polya-Gamma data augmentation.
result Improved scalability and robustness to architectural design through approximate variational inference.
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its appli…
Survey of Bayesian learning for neural networks.
problem Limitations of Bayesian learning in practical applications.
method Introduction to Bayesian Neural Networks and algorithms for inference.
result Discussion of standard and recent approaches for Bayesian inference in neural networks.
Non-negative matrix factorization (NMF) is a knowledge discovery method that is used in many fields. Variational inference and Gibbs sampling methods for it are also wellknown. However, the variational approximation error has not been clarified yet, because NMF is not statistically regular and the prior distribution us…
Paper accelerates Bayesian few-shot classification using mirror descent.
problem Non-conjugate inference in Bayesian few-shot classification.
method Integrates mirror descent-based variational inference into Gaussian process-based few-shot classification.
result Accelerated convergence and improved uncertainty quantification.
We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network which learns to transform a simple noise distribution, $p(\vecε) = \N(\vec 0,\mat I)$, to a distribution $q(\pp) := q(h(\vecε))$ over the parameters $\pp$ of another neura…