Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
arXiv:1310.5665
Abstract
Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.
Accepted at ICML 2014
References in corpus (1)
Cited by in corpus (32)
- Interpolation Consistency Training for Semi-Supervised Learning
- Dynamic Reserve Prices for Repeated Auctions: Learning from Bids
- Do Prices Coordinate Markets?
- The Pseudo-Dimension of Near-Optimal Auctions
- Optimal Reserve Price for Online Ads Trading Based on Inventory Identification
- Optimal No-regret Learning in Repeated First-price Auctions
- Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
- Machine Learning-powered Iterative Combinatorial Auctions
- Non-parametric Revenue Optimization for Generalized Second Price Auctions
- Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
- Real-Time Optimization Of Web Publisher RTB Revenues
- Learning Multi-item Auctions with (or without) Samples
- Explicit shading strategies for repeated truthful auctions
- Revenue Optimization in Posted-Price Auctions with Strategic Buyers
- Generalization Analysis for Game-Theoretic Machine Learning
- Thresholding at the monopoly price: an agnostic way to improve bidding strategies in revenue-maximizing auctions
- Loss Functions for Discrete Contextual Pricing with Observational Data
- Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
- A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
- Multi-armed Bandit Algorithm against Strategic Replication
- Learning to Clear the Market
- Learning Best Response Strategies for Agents in Ad Exchanges
- On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer
- Robust Stackelberg buyers in repeated auctions
- Algorithmic Price Discrimination
- Robust Clearing Price Mechanisms for Reserve Price Optimization
- Complexity, Stability Properties of Mixed Games and Dynamic Algorithms, and Learning in the Sharing Economy
- LP-based Approximation for Personalized Reserve Prices
- A General Framework for Evaluating Callout Mechanisms in Repeated Auctions
- Learning Theory and Algorithms for Revenue Management in Sponsored Search
- Learning to Bid Without Knowing your Value
- Censored Semi-Bandits for Resource Allocation