The paper connects neural network ensembles to Bayesian inference using variational methods.
problem Explaining the behavior of ensemble methods in neural networks.
method Deriving conditions for ensemble optimization to reduce divergence to the posterior distribution.
result Ensemble methods can be a valid alternative to approximate Bayesian inference.
A new variational inference method using optimal transport.
problem Approximating complex posterior distributions with flexible particle-based methods.
method Introducing a new particle-based variational inference method based on semi-discrete optimal transport.
result The method provides a particle approximation and optimal transportation densities.
Bayesian inference using stochastic neural networks ensembles.
problem Approximating Bayesian posterior distributions.
method Formulate stochastic ensembles of neural networks, train with variational inference, and evaluate using Monte Carlo dropout.
result Stochastic ensembles provide more accurate posterior estimates than other methods.
DPEs use KL divergence to approximate BNNs, improving uncertainty estimates for active learning.
problem Improving uncertainty estimates in active learning for visual classification.
method Regularized ensemble approach with KL divergence penalty for variational inference.
result DPEs steadily improve active learning performance with increased annotation budgets.
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.
EnKO combines VI and EnKF for efficient latent dynamics inference.
problem Particle degeneracy and biased gradient estimators in SMC-based methods.
method EnKO: hybrid of VI and EnKF.
result EnKO outperforms SMC-based methods in predictive ability and particle efficiency.
Improved neural network ensembles using Stein Variational Newton updates.
problem Lack of efficient second-order information in current ensemble methods.
method Proposes a novel approximate Bayesian inference method integrating Stein Variational Newton updates with scalable Hessian approximations.
result Significantly faster convergence and more accurate posterior distribution approximations.
Stein variational neural network ensembles improve diversity and uncertainty estimation.
problem Lack of proper Bayesian justification and diversity guarantees in deep neural network ensembles.
method Particle-based inference methods, specifically Stein variational gradient descent (SVGD), operating in weight space, function space, and hybrid settings.
result SVGD methods improve diversity and uncertainty estimation, approaching the true Bayesian posterior more closely.
Ensemble Kalman Filter improves GPSSM inference for online learning.
problem Non-mean-field variational inference issues in GPSSM.
method Combining EnKF with NMF variational inference.
result Improved online learning performance and data-fitting accuracy.
This paper improves prediction uncertainty estimation by inferring variation from neuron activation strength.
problem Estimating prediction uncertainty from ensemble methods is expensive and inaccurate.
method Introduced randomness into model training and inferred prediction variation from neuron activation strength.
result Average R squared on MovieLens is 0.56 and on Criteo is 0.81, with strong performance in variation detection.
A new Bayesian MBRL method improves performance in robotics tasks.
problem Enhancing model-based reinforcement learning with uncertainty.
method Introduces variational inference MPC and probabilistic action ensembles with trajectory sampling (PaETS).
result Consistently improves performance on challenging locomotion tasks.
New method combines variational inference with particle filtering for nonlinear data.
problem Combining variational inference and Monte Carlo sampling for nonlinear data.
method Formulates gradient steepest descent method based on local optimal transport principles, embeds local mappings in RKHS, uses approximations to avoid adjoint evaluation.
result RKHS approximation is highly successful and superior to ensemble approximation for nonlinear observational operators.
Ens-CGP synthesizes ensemble-based inference with Gaussian processes.
problem Ensemble-based inference and Gaussian process modeling.
method Formulates Ens-CGP as a conditional Gaussian process for ensemble moments.
result Ens-CGP provides a unified probabilistic foundation for Kalman-type methods.
A new framework for recycling Gaussian process approximations.
problem Efficiently combining multiple Gaussian process approximations.
method Construct variational ensembles using a dictionary of fitted Gaussian processes.
result Framework allows for various tasks and scalability.
Adaptive ensemble learning improves model accuracy and uncertainty estimates.
problem Inaccurate and uncalibrated predictions from conventional ensemble methods.
method Dependent tail-free process as ensemble weight prior, adaptive model selection, interpretable uncertainty estimates.
result Improves model performance and provides calibrated uncertainty estimates.
Bayesian neural networks struggle with accuracy and uncertainty quantification in complex models.
problem Challenges in achieving high predictive performance and reliable uncertainty estimates in Bayesian neural networks.
method Investigates computational costs, accuracy, and uncertainty quantification in Bayesian neural networks with different inference techniques.
result Variational inference provides better uncertainty quantification than Markov chain Monte Carlo, and stacking/ensembling variational approximations can achieve similar accuracy at reduced cost.
Paper improves variational inference convergence using many control variates.
problem High variance in gradient estimates hinders variational inference convergence.
method Develops a Bayesian risk minimization framework to combine many control variates.
result Combining many control variates significantly improves inference convergence.
FoRDE uses input gradients to improve neural network ensembles.
problem Improving neural network ensembles for robustness and accuracy.
method Proposes FoRDE, an ensemble learning method based on ParVI, which repels function space by input gradients.
result FoRDE significantly outperforms DEs and other ensemble methods in accuracy and calibration.
A method for efficient approximate inference on discrete distributions.
problem Applying SVGD to discrete distributions.
method Transforming discrete distributions to piecewise continuous distributions for SVGD application.
result Outperforms traditional algorithms and ensemble methods on discrete graphical models.
Novel framework improves deep learning uncertainty estimates.
problem Improving uncertainty estimates in deep learning models.
method Statistical mechanics based framework for dropout, focusing on variance of loss.
result Generates don't-know answers in classification tasks, improving classifier reliability.
Efficiently estimates neural connectivity using ensemble stimulation.
problem Estimating functional connectivity in large, behaving neural populations.
method Noisy group testing combined with convex optimization and Bayesian inference.
result Connectivity can be inferred with logarithmic growth in tests, even for large networks.
This work improves ensembling methods for neural networks to approximate Bayesian inference.
problem Uncertainty quantification in neural networks.
method Regularizing parameters about values drawn from a distribution set to the prior.
result The modified ensembling method provides more accurate uncertainty estimates than standard ensembling.
Enhances PlaNet for better planning in uncertain environments.
problem Improving deep planning networks for partially observable environments.
method Incorporates Bayesian inference to handle uncertainty in latent models and action candidates.
result Consistently improves asymptotic performance on continuous control tasks.
A new method detects outliers using ensembles of Dirichlet process mixtures.
problem Challenges in unsupervised outlier detection using Dirichlet process mixtures.
method Ensembles of Dirichlet process Gaussian mixtures with random subspace and subsampling.
result Empirically outperforms existing approaches in unsupervised outlier detection.
New method reduces parameter overhead for Bayesian neural networks.
problem High parameter overhead and difficulty of implementation in variational Bayesian neural networks.
method Constructs a general variational family for ensemble-based Bayesian neural networks that works well with batch normalization layers.
result Improves predictive accuracy and achieves almost perfect calibration on a ResNet-18 trained with ImageNet.
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.
Simple mode exploration methods do not improve performance in neural networks.
problem Improving predictive probabilities in neural networks.
method Exploring local regions around diverse solutions using simple methods.
result Simple mode exploration methods do not improve performance.
Paper improves particle variational inference by optimizing generalization error bound.
problem Improving the diversity of models in particle variational inference to enhance generalization.
method Develops a new second-order Jensen inequality with a repulsion term based on the loss function, leading to a tighter generalization error bound.
result The proposed PVI optimizes the generalization error bound directly, improving performance compared to existing methods.
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
problem Efficient inference of neuronal ensembles from activity data.
method Modified MCMC algorithm with simulated annealing for hyperparameter control.
result Our method reduces computational cost while maintaining or improving inference accuracy.
VITS improves Thompson Sampling for contextual bandits with efficient approximate inference.
problem Intractable posterior sampling in traditional Thompson Sampling for contextual bandits.
method VITS uses Gaussian Variational Inference for efficient posterior approximations.
result VITS achieves sub-linear regret bound similar to traditional TS.
Adapts to new environments in robotics using latent variable models.
problem Learning dynamics in robotic environments with subtle variations.
method Variational inference for latent representation, online Bayesian inference, neural network ensemble.
result Positive transfer during training and online adaptation on HalfCheetah task.
In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting. The variational parameters are obtained by sampling from the true posterior by gradient descent. We show our outlier detection results are comparable …
Bayesian neural networks show complex posterior distributions that HMC can capture effectively.
problem Understanding and approximating the high-dimensional, non-convex posterior of Bayesian neural networks.
method Full-batch Hamiltonian Monte Carlo (HMC) on modern architectures.
result HMC provides a robust and comparable representation of the BNN posterior, with significant performance gains over standard training and deep ensembles.
Enhances optimization and sampling methods using ensemble-based gradient inference.
problem Improving ensemble-based methods for optimization and sampling.
method Ensemble-based gradient inference (EGI) to extract higher-order derivatives from particle ensembles.
result Augmented algorithms outperform gradient-free variants, especially in multimodal and non-Gaussian settings.
Analog methods improve forecast accuracy in complex models.
problem Improving forecast accuracy in complex models like Lorenz-96.
method Constructing analogs using variational autoencoders for ensemble data assimilation.
result Constructed analogs perform as well as a full ensemble square root filter.
Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and pro…
CDNs improve uncertainty quantification in NNs with adaptive mixture components.
problem Uncertainty quantification in deep neural networks.
method Compound Density Networks (CDNs) combining Bayesian and ensemble approaches.
result CDNs provide better uncertainty estimates and robustness to adversarial examples.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
problem Stability of causal direction inference results from observational data.
method Parallel ensemble frameworks to map and improve inference accuracy.
result Significant improvement in accuracy of causal direction inference.
Bayesian methods improve OoD detection in deep networks.
problem Detecting Out-of-Distribution (OoD) inputs in deep neural networks.
method Three Bayesian inference approaches applied to VAE weights.
result Improved OoD detection scores over benchmarks.
We show that unconverged stochastic gradient descent can be interpreted as a procedure that samples from a nonparametric variational approximate posterior distribution. This distribution is implicitly defined as the transformation of an initial distribution by a sequence of optimization updates. By tracking the change …
Bayesian spatial predictive synthesis improves spatial data predictions.
problem Model misspecification and heterogeneity in spatial data.
method Bayesian ensemble methodology capturing spatially-varying model uncertainty and performance heterogeneity.
result Synthesized predictions outperform standard methods in accuracy and uncertainty quantification.
Repulsive deep ensembles improve diversity and Bayesian inference.
problem Challenges in maintaining diversity among ensemble members trained independently.
method Introducing a repulsive term in the update rule of deep ensembles.
result Training dynamics of repulsive ensembles follow a Wasserstein gradient flow of KL divergence with the true posterior.
New method creates diverse neural ensembles for better uncertainty estimation and robustness.
problem Creating more robust neural networks for uncertainty estimation and dataset shift.
method Automatically constructing ensembles with varying architectures.
result Ensembles with varying architectures outperform deep ensembles in accuracy, uncertainty calibration, and robustness.
GEnBP combines EnKF and GaBP for efficient high-dimensional inference.
problem Efficient inference in high-dimensional models.
method Gaussian Ensemble Belief Propagation algorithm combining EnKF and GaBP.
result GEnBP outperforms existing methods in accuracy and efficiency.
Repulsive ensembles improve uncertainty estimates in PINNs for differential equations.
problem Improving uncertainty estimates in PINNs for differential equations.
method Employing repulsive ensembles (RE-PINN) with a repulsive term in the loss function.
result Repulsive ensembles produce more accurate uncertainty estimates and higher sample diversity.
ProBoost boosts probabilistic classifiers by focusing on uncertain samples.
problem Improving probabilistic classifiers through targeted learning.
method ProBoost uses epistemic uncertainty to select challenging samples, increasing their weight for subsequent learners.
result ProBoost significantly improves classifier performance, especially with few weak learners.
ETGPSSM efficiently models high-dimensional, non-stationary systems with reduced complexity.
problem Prohibitive computational and parametric complexity in high-dimensional, non-stationary dynamical systems.
method ETGPSSM integrates a single shared GP with input-dependent normalizing flows for scalable and flexible modeling.
result ETGPSSM outperforms existing models in computational efficiency and accuracy.
New method reduces variance in Bayesian inverse problems.
problem High variance in Monte Carlo estimates for inverse problems.
method Conditional neural control variates based on Stein's identity.
result Substantial variance reduction across different inverse problems.