Bayesian inference in the space of functions has been an important topic for Bayesian
modeling in the past. In this paper, we propose a new solution to this problem called …

Several algorithms involving the Variational Rényi (VR) bound have been proposed to
minimize an alpha-divergence between a target posterior distribution and a variational …

This paper presents a new deep learning framework, QuantumPath, for long-term stock price
prediction, which is of great significance in portfolio management and risk mitigation …

Abstract Simulation-Based Inference (SBI) is a common name for an emerging family of
approaches that infer the model parameters when the likelihood is intractable. Existing SBI …

Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide
variety of models, from Bayesian neural networks, neural samplers and data generators to …

The Linearized Laplace Approximation (LLA) has been recently used to perform uncertainty
estimation on the predictions of pre-trained deep neural networks (DNNs). However, its …

With more and more extensive applications of target tracking, distributed multitarget tracking
(DMT) becomes an important research direction. However, the synchronization …

Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a
closed-form expression but are easy to sample from. Examples include, among others …

Recently, there has been an increasing interest in performing post-hoc uncertainty
estimation about the predictions of pre-trained deep neural networks (DNNs). Given a pre …

Bayesian optimization (BO) methods based on information theory have obtained state-of-the-
art results in several tasks. These techniques heavily rely on the Kullback-Leibler (KL) …