6 papers
Score-Based Martingale Posteriors for Deep Neural Networks
Abylay Zhumekenov, Ajay Jasra, Mohamed Maama +1
In this paper we investigate the efficacy of the score-based martingale posteriors (SMP) (Cui & Walker, 2025; Fong et al., 2023) in the context of modern and large-scale machine le…
Multilevel randomized quasi-Monte Carlo estimator for nested integration
Arved Bartuska, André Gustavo Carlon, Luis Espath +2
Nested integration problems arise in various scientific and engineering applications, including Bayesian experimental design, financial risk assessment, and uncertainty quantificat…
Quasi-Monte Carlo with a Hankel random digital net
Takashi Goda, Yang Liu, Raúl Tempone
This paper proposes a new randomized design of digital nets in which the generating matrices are chosen to be random Hankel matrices. Compared with previous randomized designs of d…
PurSAMERE: Reliable Adversarial Purification via Sharpness-Aware Minimization of Expected Reconstruction Error
Vinh Hoang, Sebastian Krumscheid, Holger Rauhut +1
We propose a novel deterministic purification method to improve adversarial robustness by mapping a potentially adversarial sample toward a nearby sample that lies close to a mode…
Efficient Stochastic BFGS methods Inspired by Bayesian Principles
André Carlon, Luis Espath, Raúl Tempone
Quasi-Newton methods are ubiquitous in deterministic local search due to their efficiency and low computational cost. This class of methods uses the history of gradient evaluations…
Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty
Arved Bartuska, Luis Espath, Raúl Tempone
Finding the optimal design of experiments in the Bayesian setting typically requires estimation and optimization of the expected information gain functional. This functional consis…