Research map / Bandits and online learning / Online convex optimization and games
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DPDS, which discretizes feasible bids and optimizes empirical payoffs with dynamic programming | Repeated multi-commodity auctions · Repeated multi-commodity auctions | regret: O(\sqrt{T\log{T}}) upper bound; \Omega(\sqrt{T}) lower bound for any strategy | Helped |
| Use DPDS instead of UCBID-GR, SA, and SVM-GR | Virtual trading on New York market historical data · NYISO virtual trading, five consecutive years | cumulative profit | Mixed |
| Use DPDS instead of SA | Simulated auction with K=5 · Simulation, budgets 13.845, 17.018, 20.870, and 25.828 | regret | Helped |
| Use DPDS instead of sliding-window forecasting | Simulated auction with K=5 · Simulation, budgets 13.845, 17.018, 20.870, and 25.828 | regret | Helped |
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