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Luowen Liu

dblp:225/2812 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0002-0698-1423ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 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 · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › mechanism design
auction design
1.012026
A Joint Auction Framework with Externalities and Adaptation · WSDM 2026
Algorithmic game theory and mechanism design › mechanism design › algorithmic mechanism design
automated mechanism design
1.012026
A Joint Auction Framework with Externalities and Adaptation · WSDM 2026

Methods — techniques the papers use, named apart from their topics

individual rationality · 1.0incentive compatibility · 1.0automated mechanism design · 1.0JEANet · 1.0
YearPublicationVenuePosition
2026 A Joint Auction Framework with Externalities and Adaptation
abstract
Recently, joint advertising has gained significant attention as an effective approach to enhancing the efficiency and revenue of advertising slot allocation. Unlike traditional advertising, which allocates advertising slots exclusively to a single advertiser, joint advertising displays advertisements from brands and stores that have established a joint selling relationship within the same advertising slot. However, existing approaches often struggle to accommodate both joint and traditional advertising frameworks, thereby limiting the revenue potential and generalizability of joint advertising. Furthermore, these methods are constrained by two critical limitations: they generally neglect the influence of global externalities, and they fail to address the bidding variability stemming from multi-party advertiser participation. Collectively, these limitations present substantial challenges to the design of joint auction mechanisms. To address these challenges, we propose a Joint Auction Framework incorporating Externalities and Adaptation, and leverage the automated mechanism design (AMD) method through our proposed JEANet to compute joint auction mechanisms that satisfy the conditions of individual rationality (IR) and approximate dominant strategy incentive compatibility (DSIC). As the first AMD method to integrate global externalities into joint auctions, JEANet dynamically adapts to the bidding characteristics of multi-party advertiser and enables unified auctions that integrate both joint and traditional advertising. Extensive experimental results demonstrate that JEANet outperforms state-of-the-art baselines in multi-slot joint auctions.
Chun Fang, Luowen Liu
WSDM2
2020 Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks
Luowen Liu, Jing Liu 0006
Soft Comput.1