Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Real-Time Bidding with Side Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Overbidding (optimistic bids from confidence-set upper bound) used to incentivize exploration of theta*, bid = max over confidence set of theta^T x | contextual bidding with unlimited budget (Section 2) · B = infinity, real-time bidding | pseudo-regret R_T | Helped |
| Lazy batch updates of the confidence set (update only when det(M_t) grows by factor 1+A) instead of every round (ablation) | unlimited-budget contextual bidding · B = infinity | pseudo-regret R_T | Mixed |
| Bid multiplier lambda found by stochastic bisection search (phases with fixed lambda_k) combined with ellipsoidal confidence sets, master phases | general limited-budget case (B = beta*T) · B linear in T | pseudo-regret R_{B,T} | Helped |
| Bid multiplier lambda learned via stochastic bisection search with known theta* (no confidence set estimation) | limited-budget bidding with known theta* · B linear in T | pseudo-regret R_{B,T} | Helped |
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