arXiv research

A new method improves inference for complex Bayesian models.

Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past decade has seen the development of auxiliary variable Monte Carlo techniques (Møller et al., 2006; Murray et al., 2006) for tackling this pr…

A large number of statistical models are "doubly-intractable": the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal likelihood (model evidence). This means that standard inference techniques to sample from the posterior, such as Markov chain Monte Carlo (…

Markov chain Monte Carlo (MCMC) algorithms are widely used to sample from complicated distributions, especially to sample from the posterior distribution in Bayesian inference. However, MCMC is not directly applicable when facing the doubly intractable problem. In this paper, we discussed and compared two existing solu…

New MCMC methods use auxiliary variables to sample from intractable distributions.

Computing partition functions, the normalizing constants of probability distributions, is often hard. Variants of importance sampling give unbiased estimates of a normalizer Z, however, unbiased estimates of the reciprocal 1/Z are harder to obtain. Unbiased estimates of 1/Z allow Markov chain Monte Carlo sampling of "d…

Doubly SGD improves convergence for intractable objective optimization.

New method for MCMC models without perfect or sequential samplers.

The exchange algorithm is studied for its convergence and asymptotic variance.

BiDVL improves EBLVMs for visual tasks by optimizing two variational distributions.

We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference …

Action-BED: Task-Driven Bayesian Experimental Design

Undirected graphical models are applied in genomics, protein structure prediction, and neuroscience to identify sparse interactions that underlie discrete data. Although Bayesian methods for inference would be favorable in these contexts, they are rarely used because they require doubly intractable Monte Carlo sampling…

New method uses neural exponential families for likelihood-free inference.

Paper extends LME models to allow sign constraints on coefficients with SDTN random effects.

Bayesian approach for inhomogeneous Poisson process intensity estimation.

Paper defines new risk measures for elliptical distributions.

A new MCMC method for GPs tackles computational burden and intractable likelihoods.

Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is largely limited by …

The paper calculates moments and conditional risks for skewed elliptical distributions.

NPE improves scalability and efficiency for ERGMs.

Improves SSL with doubly robust estimation of unlabeled class distribution.

New tests for distributional causal effects using improved kernel estimators.

CPME embeds counterfactual outcomes in RKHS for flexible policy evaluation.

Graph alignment problem solved with convex relaxations for correlated matrices.

Estimates and tests treatment effects on entire outcome distributions.

New test for binary treatment effects using kernel methods.

As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garne…

Improved off-policy evaluation for MDPs with weak distributional overlap.

New estimator for causal effects in large datasets.

We propose an algorithm which predicts each subsequent time step relative to the previous timestep of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and…

Proposes DR-ME test for interpretable distributional treatment effects.

The paper examines Einstein doubly warped product manifolds with a semi-symmetric metric connection.

This paper discusses properties of a Doubly Stochastic Poisson Process (DSPP) where the intensity process belongs to a class of affine diffusions. For any intensity process from this class we derive an analytical expression for probability distribution functions of the corresponding DSPP. A specification of our results…

Characterizes spacetimes using doubly torqued vectors.

New scalable variational Bayes methods for Hawkes processes.

New test for conditional independence using GNNs avoids estimating conditional distributions.

This paper addresses challenges in flexibly modeling multimodal data that lie on constrained spaces. Such data are commonly found in spatial applications, such as climatology and criminology, where measurements are restricted to a geographical area. Other settings include domains where unsuitable recordings are discard…

Approximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available. It relies on the use of a~model that is specified in the form of a~simulator, and approximates the likelihood at a~parameter value $…

New invariant measures doubly slice links, disproving previous bounds.

Proposes a robust method for counterfactual classification.

Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms. The distributional uncertainty size is often shown to correspond to the regularization parameter. The type of regularization (e.g. the norm used to regularize)…

Characterizes a specific type of spacetime using vector fields.

Characterizes and examines gradient solitons on doubly warped product manifolds.

The article enumerates doubly symmetric diagrams for knots up to 18 crossings.

Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013; Rezende et al., 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data. Burda et al. (2015) introduced a multi-sample v…

Proposes DR-ACI for causal effect intervals with temporal dependence.

This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly …