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Found 2 papers, 1 papers with code
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.
On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison
To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning.
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