Online Ad Allocation: Robust Optimization for Repeated Auctions
Talkat Optimization and Decision-Making Under Uncertainty (Simons Institute, September 2016) · 2016-09-19..09-23·
summary
Presents robust optimization methods for allocating online advertising in repeated auction settings, focusing on dealing with uncertainty in bidders and budgets. Discusses specific algorithmic approaches and models that adapt allocation decisions across multiple auction rounds to improve performance under uncertain inputs.
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chapters (14)
- 0:00Intro
- 2:22Online Ad Allocation: Budgeted Allocation
- 5:45Online Ad Allocation: Adversarial order
- 6:31Stochastic Model: Learn Optimal Parameters from Forecast / Distribution
- 9:33Model 1: Simultaneous Adversarial & Stochastic . Adversarial Setting: too pessimistic, real world data has
- 10:48Simultaneous Adversarial & Stochastic Approximation
- 15:11Modeling Traffic Spikes
- 24:13Empirical Study for Revenue for Offline Bundling
- 26:42Bundling with no prior commitment via Stateful Pricing
- 27:50Idea: Stateful Pricing via Bank Accounts
- 29:07Bank Accounts: Are They Optimal?
- 30:57Bank Accounts Mechanisms: Model and Results
- 32:23Bank Accounts: What's Their Structure?
- 33:45Martingale Auctions for stability over time
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