arXiv:2606.01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints. We introduce a minimalist Bayesian framework that places a prior only on the location of the optimum, while eliminating nuisance parameters through profile likelihood. This yields a generalized posterior that naturally accommodates structural constraints. As a direct instantiation, we develop MINimalist Thompson Sampling (MINTS
The continuous drive for more efficient and robust sequential decision-making in AI, coupled with the computational demands of existing Bayesian methods, creates an imperative for 'minimalist' approaches.
This research introduces a novel Bayesian framework that simplifies modeling complex systems by focusing only on optimal parameters, potentially accelerating AI development in areas requiring robust decision-making under uncertainty.
The proposed 'Minimalist Thompson Sampling' (MINTS) offers a more adaptable and computationally efficient method for incorporating structural constraints into Bayesian sequential decision-making, moving away from reliance on full probabilistic modeling.
- · AI researchers and developers
- · Generative AI
- · Adaptive systems
- · Reinforcement learning applications
- · Overly complex Bayesian methodologies
- · Computationally intensive probabilistic models
Improvements in the efficiency and robustness of AI agents for sequential decision-making tasks.
Faster development and deployment of autonomous systems capable of handling complex real-world constraints.
The proliferation of AI agents in domains currently limited by the computational complexity of existing uncertainty quantification methods.
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Read at arXiv cs.LG