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cs.LG2026
A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
Kundan Kumar, Shreya Das, Simo Särkkä
This paper proposes a Bayesian filtering-based approach for learning the dynamics of a physical system from partial, noisy measurements. We model the system dynamics using a Lagran…
cs.LG2025
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…