23 citations · 23 across the 6 of their papers we have counts for
8 papers
Designing Experiments for External Decision Makers
Sahel Iqbal, Tom Rainforth
Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals. In practice, dat…
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…
Online Bayesian Experimental Design for Partially Observed Dynamical Systems
Sara Pérez-Vieites, Sahel Iqbal, Simo Särkkä +1
Bayesian experimental design (BED) provides a principled framework for optimizing data collection by choosing experiments that are maximally informative about unknown parameters. H…
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…
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…
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
Mahdi Nasiri, Sahel Iqbal, Simo Särkkä
In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is imp…
Nesting Particle Filters for Experimental Design in Dynamical Systems
Sahel Iqbal, Adrien Corenflos, Simo Särkkä +1
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside…
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing
Hany Abdulsamad, Sahel Iqbal, Adrien Corenflos +1
Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making. Recently, control-as-inference approaches have had considerable success, provi…