Robust Learning of Optimal Auctions

07/13/2021
by   Wenshuo Guo, et al.
1

We study the problem of learning revenue-optimal multi-bidder auctions from samples when the samples of bidders' valuations can be adversarially corrupted or drawn from distributions that are adversarially perturbed. First, we prove tight upper bounds on the revenue we can obtain with a corrupted distribution under a population model, for both regular valuation distributions and distributions with monotone hazard rate (MHR). We then propose new algorithms that, given only an “approximate distribution” for the bidder's valuation, can learn a mechanism whose revenue is nearly optimal simultaneously for all “true distributions” that are α-close to the original distribution in Kolmogorov-Smirnov distance. The proposed algorithms operate beyond the setting of bounded distributions that have been studied in prior works, and are guaranteed to obtain a fraction 1-O(α) of the optimal revenue under the true distribution when the distributions are MHR. Moreover, they are guaranteed to yield at least a fraction 1-O(√(α)) of the optimal revenue when the distributions are regular. We prove that these upper bounds cannot be further improved, by providing matching lower bounds. Lastly, we derive sample complexity upper bounds for learning a near-optimal auction for both MHR and regular distributions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/27/2019

Separation between Second Price Auctions with Personalized Reserves and the Revenue Optimal Auction

What fraction of the single item n buyers setting's expected optimal rev...
research
05/17/2022

Strong Revenue (Non-)Monotonicity of Single-parameter Auctions

Consider Myerson's optimal auction with respect to an inaccurate prior, ...
research
07/21/2020

Revenue Monotonicity Under Misspecified Bidders

We investigate revenue guarantees for auction mechanisms in a model wher...
research
11/06/2019

Multi-Item Mechanisms without Item-Independence: Learnability via Robustness

We study the sample complexity of learning revenue-optimal multi-item au...
research
09/01/2017

Learning Multi-item Auctions with (or without) Samples

We provide algorithms that learn simple auctions whose revenue is approx...
research
06/08/2021

Learning to Price Against a Moving Target

In the Learning to Price setting, a seller posts prices over time with t...
research
04/20/2018

Bayesian Auctions with Efficient Queries

Generating good revenue is one of the most important problems in Bayesia...

Please sign up or login with your details

Forgot password? Click here to reset