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Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem

NeurIPS 2017 · Bandits and online learning › Online convex optimization and games · approach: Online allocation with constraints · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the COMB adversary (instead of general adversaries) in the 3-expert finite-time game3-expert finite-time prediction game · K=3 finite-time gameminimax regret gap: within 12 log2(T+1) of VTNo clear effect
Use COMB-based learner (probability from COMB value differences) instead of MWAlearner for 3-expert game · K=3 finite-time gameregret relative to MWA: MWA regret is 39% larger than the new optimal learnerHelped

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