Solves imaging inverse problems using a VAE prior and joint MAP optimization.
problem Solving ill-posed inverse problems in imaging.
method Joint Posterior Maximization with a VAE prior, using alternate optimization algorithms and stochastic encoding.
result Converges to high-quality solutions close to bi-convex, outperforming non-convex MAP approaches.
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint …
A new method approximates posterior for VAEs without iterative training.
problem Inference models in VAEs are poor early on, leading to suboptimal models.
method Train generative and inference models independently, using a model-agnostic posterior approximation (MAPA).
result MAPA approximates the true posterior deterministically and can improve density estimation.
Proposes methods to estimate posterior probability and propensity score functions without assuming constant propensity score.
problem Learning from biased positive-unlabeled data.
method Parametric approach to joint estimation of posterior probability and propensity score functions using maximum likelihood and alternating maximization.
result Proposed methods are comparable or better than existing methods based on Expectation-Maximisation scheme.
New method uses diffusion models to optimize experimental design efficiently.
problem Optimizing experimental design for high-dimensional and complex settings.
method Introduces a pooled posterior distribution and uses diffusion-based samplers for efficient sampling and optimization.
result Extends Bayesian Optimal Experimental Design to practical scenarios.
Solves imaging inverse problems using autoencoding priors.
problem Ill-posed inverse problems in imaging.
method Joint Posterior Maximization with Autoencoding Prior (JPMAP).
result JPMAP converges to a stationary point and provides robust solutions.
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most signifi…
New method uses joint stochastic approximation to improve learning of discrete latent models.
problem Challenges in learning discrete latent variable models, especially with inference model gradients and log-likelihood optimization.
method Proposes a new method based on stochastic approximation theory that directly maximizes the target log-likelihood and minimizes the posterior-inference model divergence.
result Consistently outperforms recent competitive algorithms in generative modeling and structured prediction tasks.
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by maximizing a lower bound on the marginal likelihood. This requires the ability to integrate a sum of terms in the log joint likelihood using …
FJS method improves multinomial classification accuracy.
problem Improving multinomial classification accuracy under dataset shift.
method Derive FJS representation and propose alternative methods.
result Factorizable joint shift is not fully identifiable without additional assumptions.
In this paper we address the problem of tracking multiple speakers via the fusion of visual and auditory information. We propose to exploit the complementary nature of these two modalities in order to accurately estimate smooth trajectories of the tracked persons, to deal with the partial or total absence of one of the…
Posterior Matching enables VAEs to model arbitrary conditional densities.
problem Modeling conditional dependencies in unsupervised learning.
method Posterior Matching framework for arbitrary conditioning.
result Posterior Matching enables VAEs to perform arbitrary conditioning without modification.
New framework improves variational inference for high-dimensional posteriors.
problem Challenges in choosing variational objectives and approximating families for high-dimensional posteriors.
method Conceptual framework and experimental tools to understand and optimize variational objectives and families.
result For moderate-to-high-dimensional posteriors, exclusive KL divergence is recommended due to optimization ease; for low-dimensional, heavy-tailed variational families are effective.
Robust VB framework handles contamination using min-max median aggregation.
problem Handling contamination and outliers in datasets.
method Partition data into subsets, formulate robust optimization problem, use min-max median KL divergence.
result Min-max median formulation improves robustness and statistical rates.
Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously. The log-uniform prior not only interprets the regularization capacity of Gaussian dropout in netw…
The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters. Unfo…
QEM uses parallel importance weighting for fast approximate Bayesian inference.
problem Bayesian inference challenges in large models with many observations and latent variables.
method Expectation Maximization (EM) with massively parallel importance weighting.
result QEM is faster and more scalable than RWS and VI.
Improved multimodal variational models capture more complex joint distributions.
problem Limited expressiveness of multimodal variational models.
method Used normalizing flows to approximate and transform a simple parametric joint posterior into a more complex one.
result The model improves on state-of-the-art multimodal variational methods on various tasks.
A new method infers graph structure and parameters using a single generative flow network.
problem Bayesian Network structure and parameter inference from data.
method Single GFlowNet with two-phase sampling: DAG generation followed by parameter assignment.
result Accurate approximation of joint posterior distribution over graph structure and parameters.
Bayesian linear networks reveal optimal depth and width trade-offs.
problem Understanding how depth, width, and dataset size affect model quality in linear networks.
method Zero noise Bayesian inference with Gaussian weight priors and mean squared error.
result Optimal predictions at infinite depth and maximized Bayesian model evidence at infinite depth.
Anosov subgroup equidistributes geodesics and holonomies on homogeneous spaces.
problem Equidistribution of geodesics and holonomies in Anosov homogeneous spaces.
method Analyzes maximal flat cylinders and their holonomies for Anosov subgroups.
result Joint equidistribution of maximal flat cylinders and holonomies as circumference tends to infinity.
Bayesian framework improves robustness in nonlinear regression models.
problem Measurement error, model misspecification, and distributional misspecification in regression analyses.
method Joint Dirichlet process prior on latent covariate-response distribution, updating with posterior pseudo-samples.
result Improved stability and consistency in estimators under increasing measurement error.
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task i…
Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer…
A new framework CyGen models joint distributions using cyclic conditionals.
problem Modeling a joint distribution using only two conditional models without relying on an uninformative prior.
method Developed a general theory for operable equivalence criteria for compatibility and sufficient conditions for determinacy. Proposed CyGen framework and methods to achieve compatibility and determinacy.
result CyGen better fits data and captures more representative features compared to models using an uninformative prior.
SJS model predicts label shifts in multinomial datasets.
problem Predicting label shifts in multinomial datasets.
method Sparse joint shift model for dataset shift.
result Valid predictions and class prior probabilities estimates.
Method generates joint posterior samples of source and foreground mass distributions for gravitational lensing.
problem Challenging inference problem for high-resolution, high signal-to-noise ratio gravitational lensing.
method Combines diffusion-based generative modeling and recurrent inference machines.
result Can model realistic gravitational lensing simulations down to the noise level.
Novel method for SDE calibration from sparse data using neural flows.
problem Calibrating SDEs from sparse, noisy observations.
method Characterization of posterior SDE using neural networks trained to solve a PDE with multiplicative updates.
result Significant improvement in scalability and accuracy compared to classical methods.
Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexit…
A new filter adapts to heavy-tailed data without tuning, improving performance in challenging conditions.
problem Degraded performance of Kalman and EnKF in heavy-tailed distributions.
method Generalizes EnKF using t-distributions, estimating parameters via EM algorithm.
result Improves performance on challenging filtering problems with heavy-tailed noise.
Optimal securities lending mechanism incentivizes truthfulness and privacy.
problem Maximizing resource usage in securities lending while ensuring truthful reporting and privacy.
method Bayesian optimal algorithm adapted for differential privacy, combined with market equilibrium dynamics.
result An algorithm that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful.
This paper offers a simple method for Bayesian regression with unknown transformations.
problem Joint inference of unknown transformations and model parameters in Bayesian regression is computationally inefficient and cumbersome.
method The paper introduces a Bayesian nonparametric model via the Bayesian bootstrap to directly target the posterior distribution of the transformation.
result The approach delivers joint posterior consistency and efficient Monte Carlo inference for the transformation and all parameters.
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names. Informed by cognitive research, we propose an approach based on sharing context based object hypotheses between visual and lexical spaces. To this end, we present the Visual Seman…
DiBS learns Bayesian network structure and parameters efficiently.
problem Bayesian structure learning with uncertainty reasoning.
method Differentiable framework for continuous latent graph representation, agnostic to local conditional distributions.
result Significantly outperforms related approaches in posterior inference.
The paper introduces a new Wasserstein distance for approximating posteriors in inverse problems.
problem Approximating posterior measures in inverse problems using conditional Wasserstein distances.
method Introduces a conditional Wasserstein distance with restricted couplings and derives its dual.
result Shows that conditional Wasserstein GANs can yield favorable properties for posterior sampling.
Develops a Bayesian framework for portfolio choice with a new posterior distribution.
problem Estimation risk in parametric portfolio policies.
method Generalized Bayesian framework with Gibbs posterior, utility maximization, and KNEEDLE algorithm.
result Optimal scaling parameter λ controls the balance between prior and data. New method estimates HMM hidden states efficiently.
problem Inaccurate posterior predictive distribution in HMMs.
method Autoregressive-flow for estimating hidden states.
result Estimates comparable to SMC algorithm.
New decision-theoretic characterization separates belief and decision posteriors.
problem Understanding the conditions under which loss-based updating coincides with Bayesian updating.
method Decision-theoretic approach to distinguish belief and decision posteriors.
result Generalized Bayes coincides with ordinary Bayesian updating only if the loss is proportional to negative log-likelihood.
New methods for scalable inference in modular models with misspecified sub-models.
problem Model misspecification in multi-modular models complicates evidence combination.
method Variational methods for approximating Cut and SMI posteriors, and Variational Meta-Posterior.
result Feasibility of analysis with multiple cuts using a single set of variational parameters.
Enhances multimodal generation with Normalizing Flows and correlation analysis.
problem Generating coherent cross-modal data from multiple sources.
method Uses Deep Canonical Correlation Analysis for shared information, Normalizing Flows for diversity, and Product of Experts for scalability.
result Improves likelihood, diversity, and coherence in conditional generation.
Online method for state estimation and parameter learning in SSMs.
problem State estimation and parameter learning in state-space models.
method Stochastic gradient optimization of variational lower bound, using backward decompositions and Bellman recursions.
result Ability to operate online without revisiting historic observations.
Efficiently estimates marginal posteriors for complex simulations.
problem Bayesian inference in high-dimensional, intractable likelihood scenarios.
method Simulates and estimates low-dimensional marginal posteriors, using truncated indicators.
result Simulator efficiency and robustness testing of inference results.
Training deep generative models with maximum likelihood remains a challenge. The typical workaround is to use variational inference (VI) and maximize a lower bound to the log marginal likelihood of the data. Variational auto-encoders (VAEs) adopt this approach. They further amortize the cost of inference by using a rec…
Bayesian inference on structured models typically relies on the ability to infer posterior distributions of underlying hidden variables. However, inference in implicit models or complex posterior distributions is hard. A popular tool for learning implicit models are generative adversarial networks (GANs) which learn pa…
New method selects features via tensor decomposition and submodular optimization.
problem Feature selection for high-dimensional data.
method Low-rank tensor model, submodular optimization, greedy algorithm.
result Proposed method outperforms state-of-the-art feature selection.
MINIMALIST maximizes mutual information for likelihood estimation from simulated data.
problem Learning model parameters from likelihood functions that cannot be computed.
method Maximizes mutual information between simulated data and model parameters using neural networks.
result Different methods aiming at the same optimal energy form can be directly benchmarked.
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.
Bayesian neural networks explore rare fluctuations for better feature learning.
problem Understanding rare but dominant fluctuations in Bayesian neural networks.
method Large-deviation theory and joint optimization over predictors and internal kernels.
result Posterior rate function optimization reveals data-dependent kernel selection.