Demo Data from VLDB 2026 / reviewers in the wild / expert
Jing Yu 0010
dblp:42/6466-10
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-1318-0189ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Mathematical optimization · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization › convergence analysis
non-asymptotic analysis |
0.8 | 1 | 2024 | Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024 |
Mathematical optimization › control theory
robust control |
0.8 | 1 | 2024 | Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024 |
Mathematical optimization › control theory › system identification
set membership estimation |
0.8 | 1 | 2024 | Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024 |
Mathematical optimization › control theory
system identification |
0.8 | 1 | 2024 | Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024 |
Methods — techniques the papers use, named apart from their topics
set membership estimation · 0.8least-squares estimation · 0.8convergence rate analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic AnalysisabstractThis paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets are crucial for the quality of robust control since they directly influence the conservativeness of the control design. Departing from the confidence region analysis of least squares estimation, this paper focuses on set membership estimation (SME). Though good numerical performances have attracted applications of SME in the control literature, the non-asymptotic convergence rate of SME for linear systems remains an open question. This paper provides the first convergence rate bounds for SME and discusses variations of SME under relaxed assumptions. We also provide numerical results demonstrating SME’s practical promise. Yingying Li 0005, Jing Yu 0010, Lauren E. Conger, Taylan Kargin, Adam Wierman |
ICML | 2 |