Establishes a regret lower bound proving that polynomial effective optimism rules out minimax-optimal rates for GP-UCB on Matérn kernels under uniform confidence.
Gel E, Ntaimo L, eds., Recent Advances in Optimization and Modeling of Contemporary Problems, 255--278, INFORMS TutORials in Operations Research (INFORMS)
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LLM-MAS uses prompt-embedded design choices to drive multi-agent LLM simulations modeled as a controlled Markov chain, with an on-trajectory algorithm for zeroth-order gradient-based optimization of steady-state performance.
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On the Suboptimality of GP-UCB under Polynomial Effective Optimism
Establishes a regret lower bound proving that polynomial effective optimism rules out minimax-optimal rates for GP-UCB on Matérn kernels under uniform confidence.
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Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
LLM-MAS uses prompt-embedded design choices to drive multi-agent LLM simulations modeled as a controlled Markov chain, with an on-trajectory algorithm for zeroth-order gradient-based optimization of steady-state performance.