Demo Data from EDBT 2026 / reviewers in the wild / expert
Tingwei Hu
dblp:355/3038
· DBLP profile ↗
4ranked-venue papers
3as first author
4since 2021 · last 2026
0009-0005-0844-7506ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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 |
Mathematical optimization · 67% Approximation and online algorithms · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Approximation and online algorithms
approximation algorithms |
1.0 | 1 | 2026 | Near-Tight Approximation Algorithms for Bottleneck Multiple Knapsack Problems · ICALP 2026 |
Mathematical optimization
discrete optimization |
1.0 | 1 | 2026 | Near-Tight Approximation Algorithms for Bottleneck Multiple Knapsack Problems · ICALP 2026 |
Mathematical optimization › knapsack problem
multiple knapsack |
1.0 | 1 | 2026 | Near-Tight Approximation Algorithms for Bottleneck Multiple Knapsack Problems · ICALP 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Near-Tight Approximation Algorithms for Bottleneck Multiple Knapsack ProblemsabstractIn the bottleneck multiple knapsack problem, we are given a set of items and a set of knapsacks, where each item has a profit and a weight, and each knapsack has a capacity. Our goal is to assign items to knapsacks so as to maximize the minimum profit received by any knapsack subject to the capacity constraint. When all knapsacks have identical capacity, we give a (2/3 - ε)-approximation algorithm for any constant ε > 0. This result almost matches the (2/3 + ε) inapproximability bound for the bottleneck multiple subset sum problem (Caprara et al., 2000). When the knapsacks can have arbitrary capacities, we propose a (1/2 - ε)-approximation algorithm for any constant ε > 0. We also prove a hardness bound of (1/2 + ε) for any constant ε > 0. Lin Chen 0009, Tingwei Hu, Yuchen Mao 0001, Yong Chen 0002, Lili Mei, An Zhang 0001, Guangting Chen, Guochuan Zhang |
ICALP | 2 |
| 2026 | Nearly tight bounds on the price of fairness for indivisible items under budget constraints
Tingwei Hu, Lili Mei, Zhen Wang 0013, Guochuan Zhang |
Theor. Comput. Sci. | 1 |
| 2024 | The Price of Fairness for Budget-Feasible EF1 Allocations
Tingwei Hu, Lili Mei, Zhen Wang 0013, Guochuan Zhang |
COCOA (1) | 1 |
| 2023 | Stackelberg Strategies on Epidemic Containment Games
Tingwei Hu, Lili Mei, Zhen Wang 0013 |
IJTCS-FAW | 1 |