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Near-Optimal Design of Experiments via Regret Minimization

ICML 2017 · Bandits and online learning › Online convex optimization and games · approach: Online linear-quadratic control regret · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Algorithm 1 vs uniform samplingExperimental design · Synthetic k=2pfE objective: 349.4 -> 53.52Helped
Algorithm 1 vs Fedorov's exchangeExperimental design · Synthetic k=2pfE objective: 172.9 -> 53.52Helped
Algorithm 1 vs Fedorov's exchangeExperimental design · Synthetic k=2pfG objective: 117.7 -> 90.77Helped
Algorithm 1 vs weighted samplingExperimental design · Synthetic k=2pfT objective: Inf -> 1.19Helped
Algorithm 1 vs Fedorov's exchangeExperimental design · Synthetic k=2pfD objective: 5.52 -> 4.72No clear effect
Algorithm 1 vs Fedorov's exchange on running timeAlgorithm 1 · Synthetic k=2p, fGrunning time (s): 8893 -> < 1Helped
Algorithm 1 vs Fedorov's exchangeExperimental design · Minnesota wind, fV designfV objective: 10.0 -> 10.8No clear effect
Algorithm 1 vs Fedorov's exchangeExperimental design · Minnesota wind, fV designMSE: 0.86 -> 0.72Helped
Algorithm 1 vs Fedorov's exchangeExperimental design · Minnesota wind, fG designMSE: 0.78 -> 0.76Helped
Algorithm 1 vs uniform samplingExperimental design · Minnesota wind, fG designfG objective: 3093 -> 29.2Helped
Algorithm 1 vs weighted samplingExperimental design · Minnesota wind, fV designfV objective: 21.4 -> 10.8Helped
increase k from 2p to 3pAlgorithm 1 · SyntheticfE objective: 53.52 -> 41.53Helped

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