Demo Data from VLDB 2026 / reviewers in the wild / expert
Marissa Beck
dblp:163/6360
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › mechanism design › contract theory
adverse selection |
0.2 | 1 | 2015 | Adverse Selection and Auction Design for Internet Display Advertising · EC 2015 |
Algorithmic game theory and mechanism design › mechanism design
auction design |
0.2 | 1 | 2015 | Adverse Selection and Auction Design for Internet Display Advertising · EC 2015 |
Algorithmic game theory and mechanism design › mechanism design › auction design
display advertising auction |
0.2 | 1 | 2015 | Adverse Selection and Auction Design for Internet Display Advertising · EC 2015 |
Algorithmic game theory and mechanism design
mechanism design |
0.2 | 1 | 2015 | Adverse Selection and Auction Design for Internet Display Advertising · EC 2015 |
Algorithmic game theory and mechanism design › mechanism design
truthful mechanism |
0.2 | 1 | 2015 | Adverse Selection and Auction Design for Internet Display Advertising · EC 2015 |
Methods — techniques the papers use, named apart from their topics
game theory · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Adverse Selection and Auction Design for Internet Display AdvertisingabstractWe model an online display advertising environment with brand advertisers and better-informed performance advertisers, and seek an auction mechanism that is strategy-proof, anonymous and insulates brand advertisers from adverse selection. We find that the only such mechanism that is also false-name proof assigns the item to the highest bidding performance advertiser only when the ratio of the highest bid to the second highest bid is sufficiently large. For fat-tailed match-value distributions, this new mechanism captures most of the gains from good matching and improves match values substantially compared to the common practice of setting aside impressions in advance. Nick Arnosti, Marissa Beck, Paul Milgrom |
EC | 2 |