arXiv research

Improved Gibbs sampler speeds up Bayesian exponential smoothing model.

Exponentially smoothed RNNs improve industrial forecasting.

Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.

This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard t…

Introduces a new theoretical framework for exponential smoothing.

The study analyzes when Bayesian averaging over decision trees is reliable.

Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper exp…

Bayesian posterior contraction rates improve with decreasing tails

Auto-regressive models improve smoothing efficiency with exponentially tapered windows.

Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computat…

We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks …

This paper develops a mathematical and computational framework for analyzing the expected performance of Bayesian data fusion, or joint statistical inference, within a sensor network. We use variational techniques to obtain the posterior expectation as the optimal fusion rule under a deterministic constraint and a quad…

GES algorithm improves consistency for nonparametric DAG models.

As data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and generate a score for each that takes all of the observed data into account. For instance, Bayesian networks, the model chosen in this paper, ha…

We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the opti…

Bayesian approach generalizes ADMM for federated learning.

Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential smoothing is used in all these domains to obtain simple interpretable models of time series and to forecast future values. Despite its popularity, expon…

Novel Bayesian method for high-dimensional count data prediction.

CAVI converges exponentially fast for Bayesian PCA models.

This work improves bounds on Bayesian coreset quality.

NatPN provides fast, accurate uncertainty estimation for exponential family distributions.

This paper extends exponential smoothing to distributional time series using Wasserstein distance.

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it work…

Bayesian networks are typically faithful, with implications for causal inference.

This paper develops efficient federated learning and unlearning methods in Bayesian models.

News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable est…

BART is extended to handle various response variables.

In this paper, we derive a Bayesian model order selection rule by using the exponentially embedded family method, termed Bayesian EEF. Unlike many other Bayesian model selection methods, the Bayesian EEF can use vague proper priors and improper noninformative priors to be objective in the elicitation of parameter prior…

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

Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.

Smooth solutions found for hydrodynamic equations.

Untuned SGD converges but with an exponential dependence on smoothness, adaptive methods prevent this.

In this paper, we examine the dependence of standard gluing process for pseudoholomorphic curves under the change of the length $T$ of the neck-region with respect to the cylindrical metrics associated to the given analytic coordinates near the punctures in the setting of bordered open Riemann surface with boundary pun…

In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised for the case, where the prior distribution belongs to the exponential family of distributions. The logarithm of the likelihood function is li…

Adaptive algorithm identifies best arm with abstention, showing phase transition from polynomial to exponential error probability.

Bayesian regularization tackles collinearity in large-scale systems with correlated inputs.

Geometric analysis improves convergence of variational inference.

BKTF uses tensor factorization for Bayesian optimization of complex functions.

New CTBNs with clocks allow for non-exponential survival times.

It is known that describing or calculating the conditional probabilities of multiple events is exponentially expensive. In this work, Bayesian tensor network (BTN) is proposed to efficiently capture the conditional probabilities of multiple sets of events with polynomial complexity. BTN is a directed acyclic graphical …

Deep-HGP uses Bayesian nonparametric approach for complex data regression.

QBVI uses natural gradients for efficient Bayesian learning.

Continuous control imitation learning fails if expert actions are smooth.

Conditional Autoregressive Value-at-Risk and Conditional Autoregressive Expectile have become two popular approaches for direct measurement of market risk. Since their introduction several improvements both in the Bayesian and in the classical framework have been proposed to better account for asymmetry and local non-l…

Extends diffusion models to handle exponential family distributions for inverse problems.

Smoothing graphons improve link prediction in Bayesian SBM without increasing computational complexity.

Conjugate pairs of distributions over infinite dimensional spaces are prominent in statistical learning theory, particularly due to the widespread adoption of Bayesian nonparametric methodologies for a host of models and applications. Much of the existing literature in the learning community focuses on processes posses…

Decentralized Bayesian learning reduces KL-divergence exponentially.