Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shuyu Bao
dblp:320/6739
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-4959-5047ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021
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 · 77% Approximation and online algorithms · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › non-cooperative game
facility location game |
0.9 | 1 | 2025 | Facility Location Games with Optional Preferences: A Revisit · AAAI 2025 |
Approximation and online algorithms › approximation algorithms
approximation ratio analysis |
0.3 | 1 | 2025 | Facility Location Games with Optional Preferences: A Revisit · AAAI 2025 |
Methods — techniques the papers use, named apart from their topics
strategyproof mechanism design · 0.9approximation ratio analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Facility Location Games with Optional Preferences: A RevisitabstractWe study the k-facility location games with optional preferences on the line. In the games, each strategic agent has a public location preference on the k facility locations and a private optional preference on the preferred/acceptable set of facilities out of the k facilities. Our goal is to design strategyproof mechanisms to elicit agents’ optional preferences and locate k facilities to minimize the social or maximum cost of agents based on their facility preferences and public agent locations. We consider two variants of the facility location games with optional preferences: the Min variant and the Max variant where the agent’s cost is defined as their distance to the closest acceptable facility and the farthest acceptable facility, respectively. For the Min variant, we present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost with k ≥ 3 facilities, achieving approximation ratios of 3 and 2n+1 respectively. We complement the results by establishing lower bounds of 3/2 and n/4 for the approximation ratios achievable by any deterministic strategyproof mechanisms for the maximum cost and social cost, respectively. We then improve our results in a special setting of the Min variant where there are exactly three facilities and present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost. For the Max variant, we present an optimal deterministic strategyproof mechanism for the maximum cost and a k-approximation deterministic strategyproof mechanism for the social cost. Xingchen Sha, Shuyu Bao, Hau Chan, Vincent Chau, Ken C. K. Fong, Minming Li |
AAAI | 2 |
| 2023 | A fast and deterministic algorithm for Knapsack-constrained monotone DR-submodular maximization over an integer lattice
Suning Gong, Qingqin Nong, Shuyu Bao, Qizhi Fang, Ding-Zhu Du |
J. Glob. Optim. | 3 |