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Jing Yu 0010

dblp:42/6466-10 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Mathematical optimization › convergence analysis
non-asymptotic analysis
0.812024
Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024
Mathematical optimization › control theory
robust control
0.812024
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
0.812024
Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis · ICML 2024
Mathematical optimization › control theory
system identification
0.812024
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
YearPublicationVenuePosition
2024 Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis
abstract
This 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
ICML2