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Batched Gaussian Process Bandit Optimization via Determinantal Point Processes

NeurIPS 2016 · Probabilistic and scientific modeling › Bayesian optimization · approach: Acquisition function design · 32 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Sample batches via DPP-SAMPLE instead of greedy DPP-MAXUCB/EST-DPP · Branin-Hoo and Hartmann-6, B=5 and 10immediate regretHelped
Use DPP-MAX instead of batched BUCB/B-ESTUCB/EST-DPP-MAX · robotimmediate regretHelped
Increase batch size from 5 to 10 for DPP-SAMPLE vs DPP-MAXDPP-SAMPLE · robotimmediate regretHelped
Use LP-UCB instead of UCB-DPP-SAMPLELP-UCB · robotimmediate regretHurt
Use LP-UCB instead of DPP-MAXLP-UCB · Hartmann-6immediate regretHelped
Use DPP-SAMPLE instead of LP-UCBDPP-SAMPLE · Hartmann-6immediate regretHelped
Increase batch size from 5 to 10LP-UCB · Branin-Hooimmediate regretNo clear effect
Use DPP methods instead of batched counterpartsDPP-MAX and DPP-SAMPLE · FastXML Bibtex Prec@1immediate regretHelped
Use DPP-SAMPLE instead of DPP-MAX, B=5DPP-SAMPLE · FastXML Bibtex Prec@1immediate regretMixed
Use LP-UCB instead of UCB-DPP-MAX, B=10LP-UCB · FastXML Bibtex Prec@1immediate regretHelped
Use DPP-SAMPLE instead of LP-UCBDPP-SAMPLE · Abaloneimmediate regretHelped
Use LP-UCB, B=5 vs B=10LP-UCB · FastXML Deliciousimmediate regretMixed

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