5 papers
Recursive Entropic Variational Inference for Nonlinear State-Space Models
Hany Abdulsamad, Ãngel F. GarcÃa-Fernández, Simo Särkkä
We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangian formulation that casts Bayesi…
Maximin Robust Bayesian Experimental Design
Hany Abdulsamad, Sahel Iqbal, Christian A. Naesseth +2
We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between the experimenter and an adversarial na…
Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
Hany Abdulsamad, Sahel Iqbal, Simo Särkkä
Optimal decision-making under partial observability requires agents to balance reducing uncertainty (exploration) against pursuing immediate objectives (exploitation). In this pape…
A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control
Casian Iacob, Hany Abdulsamad, Simo Särkkä
Model predictive control (MPC) is a powerful framework for optimal control of dynamical systems. However, MPC solvers suffer from a high computational burden that restricts their a…
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites +2
This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangea…