Demo Data from EDBT 2026 / reviewers in the wild / expert
Runqian Wang
dblp:266/7977
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2024
0000-0003-3856-6352ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 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.
| Computer networks
1 paper |
Cellular and mobile networks · 67% Wireless networking · 17% Network performance modeling · 17% | |
| Artificial intelligence
1 paper |
Efficient and distributed learning · 67% Transfer learning and domain adaptation · 33% |
Topics — the 9 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › parameter-efficient fine-tuning
low-rank adaptation |
0.8 | 1 | 2024 | Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024 |
Machine learning › Transfer learning and domain adaptation › knowledge transfer
model transfer |
0.8 | 1 | 2024 | Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024 |
Machine learning › Efficient and distributed learning
parameter-efficient fine-tuning |
0.8 | 1 | 2024 | Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024 |
Cellular and mobile networks
device-to-device communication |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Network performance modeling › stochastic geometry modeling
meta distribution |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Cellular and mobile networks › millimeter-wave communication
millimeter wave cellular networks |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Cellular and mobile networks
millimeter-wave communication |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Wireless networking
stochastic geometry |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Cellular and mobile networks › device-to-device communication
underlaying cellular networks |
0.5 | 1 | 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021 |
Methods — techniques the papers use, named apart from their topics
low-rank adaptation · 0.8nakagami fading · 0.5moment analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning
Runqian Wang, Soumya Ghosh, David D. Cox, Diego Antognini, Aude Oliva, Rogério Feris, Leonid Karlinsky |
NeurIPS | 1 |
| 2021 | Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular NetworksabstractThis paper provides a deep analysis for millimeter-wave device-to-device (mm-wave D2D) underlaid cellular networks to investigate how the performance of individual users or link percentiles under constraints are affected by the interaction between D2D and cellular communication modes integrated with the salient mm-wave features. Under a general Nakagami fading model, we derive the upper bounds for the$b$-moments of the conditional signal-to-interference-noise ratio (SINR) distributions for the two modes given the network realization, and further calculate the upper bounds of SINR meta distributions (MDs). Moreover, we extend the MD-based analysis to evaluate the fine-grained performance for the transmission effectiveness, through deriving the bounding results for the user-perceived rate MD and spatial rate capacity (SRC), the latter of which is defined as the effective density of users satisfying a given transmission rate requirement with a certain probability. Numerical results validate the tightness of the proposed bounds and reveal the different effects of the network configuration parameter associated with mm-wave on the link and network performances from the view of individual users. Runqian Wang, Na Deng, Haichao Wei |
IEEE Trans. Commun. | 1 |