TACC algorithm for adaptive multi-fidelity bandits with improving proxies achieves instance-dependent regret by replacing logarithmic high-fidelity pulls with bounded low-fidelity continuation for intermediate arms.
Put cash on bandits: A max k-armed problem for automated machine learning.arXiv preprint arXiv:2505.05226
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PS-PFN extends posterior sampling to the max k-armed bandit setup using PFNs for in-context posterior estimation of maximal pipeline performance, outperforming other bandit and AutoML strategies on benchmarks.
The authors describe design considerations and fabrication of 40x65x80 nm nanodiamond pillars with single NV centers as a step toward matter-wave interferometry of massive objects.
citing papers explorer
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Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies
TACC algorithm for adaptive multi-fidelity bandits with improving proxies achieves instance-dependent regret by replacing logarithmic high-fidelity pulls with bounded low-fidelity continuation for intermediate arms.
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In-Context Decision Making for Optimizing Complex AutoML Pipelines
PS-PFN extends posterior sampling to the max k-armed bandit setup using PFNs for in-context posterior estimation of maximal pipeline performance, outperforming other bandit and AutoML strategies on benchmarks.
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Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects
The authors describe design considerations and fabrication of 40x65x80 nm nanodiamond pillars with single NV centers as a step toward matter-wave interferometry of massive objects.