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FLARE MCMC is a novel multi-fidelity method that enhances Markov chain Monte Carlo sampling by using layer-adaptive recursive proposals with lower-fidelity likelihood approximations, demonstrating improved efficiency in applications such as hydrology and cosmology.
The paper tackles multi-fidelity Bayesian optimization where the highest-fidelity function is too expensive to be part of the optimization loop, and proposes incorporating historical high-fidelity data with task descriptors. The method is demonstrated on synthetic functions, chemistry, and hyperparameter optimization tasks.
This paper introduces an adaptive on-the-fly multifidelity machine learning algorithm for quantum chemistry that autonomously determines training data composition across fidelities, reducing data generation costs by up to 30x compared to single-fidelity methods and up to 5x compared to standard multifidelity methods.
The paper proposes 2FFS, a two-fidelity tree-search algorithm that adaptively balances cheap biased evaluations with expensive accurate evaluations in stochastic minimax trees for fixed-confidence best-action identification, with theoretical guarantees and experimental efficiency gains.
This paper introduces AutoLLMResearch, an agentic framework that automates the configuration of expensive LLM experiments by learning from low-fidelity environments and extrapolating to high-cost settings. It aims to reduce computational waste and reliance on expert intuition in scalable LLM research.