1 citations · 1 across the 5 of their papers we have counts for
5 papers
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
Nicolas Menet, Andreas Krause, Abbas Rahimi
Balancing exploration with exploitation is a core challenge in black-box optimization and reinforcement learning. We introduce POETS (licy nsembles for $\t…
A Theoretical Analysis of Test-Driven Code Generation
Nicolas Menet, Michael Hersche, Andreas Krause +1
Code assistants are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored.…
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet, Aleksandar Terzić, Michael Hersche +2
Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We prop…
LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie +1
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challeng…
Bandits with Preference Feedback: A Stackelberg Game Perspective
Barna Pásztor, Parnian Kassraie, Andreas Krause
Bandits with preference feedback present a powerful tool for optimizing unknown target functions when only pairwise comparisons are allowed instead of direct value queries. This mo…