Trung Dang 0001

dblp:267/3239-1 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0002-9914-5009ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 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
Algorithmic game theory and mechanism design · 83% Approximation and online algorithms · 17%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
mechanism design
0.912025
A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization · SODA 2025
Algorithmic game theory and mechanism design › mechanism design › algorithmic mechanism design
multi-dimensional mechanism design
0.912025
A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization · SODA 2025
Algorithmic game theory and mechanism design
revenue maximization
0.912025
A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization · SODA 2025
Approximation and online algorithms
contention resolution schemes
0.312025
A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization · SODA 2025
Approximation and online algorithms
online algorithms
0.312025
A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization · SODA 2025
YearPublicationVenuePosition
2025 Robust Max Selection
Trung Dang 0001
ISIT1
2025 A Multi-Dimensional Online Contention Resolution Scheme for Revenue Maximization
abstract
We study multi-buyer multi-item sequential item pricing mechanisms for revenue maximization with the goal of approximating a natural fractional relaxation - the ex ante optimal revenue. We assume that buyers’ values are subadditive but make no assumptions on the value distributions. While the optimal revenue, and therefore also the ex ante benchmark, is inapproximable by any simple mechanism in this context, previous work has shown that a weaker benchmark that optimizes over so-called “buy-many” mechanisms can be approximated. Approximations are known, in particular, for settings with either a single buyer or many unit- demand buyers. We extend these results to the much broader setting of many subadditive buyers. We show that the ex ante buy-many revenue can be approximated via sequential item pricings to within an O (log2 m ) factor, where m is the number of items; a logarithmic dependence on m is also necessary.
Shuchi Chawla 0001, Dimitris Christou, Trung Dang 0001, Gregory Kehne, Rojin Rezvan
SODA3