Demo Data from EDBT 2026 / reviewers in the wild / expert
Huanran Lu
dblp:150/6241
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2
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
2 papers |
Mathematical optimization · 77% Algorithms and data structures · 23% | |
| Artificial intelligence
1 paper |
Time series and sequential data · 67% Learning theory · 33% |
Topics — the 9 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › high-dimensional statistics
high-dimensional estimation |
0.2 | 1 | 2015 | A direct estimation of high dimensional stationary vector autoregressions · J. Mach. Learn. Res. 2015 |
Machine learning › Time series and sequential data
time series analysis |
0.2 | 1 | 2015 | A direct estimation of high dimensional stationary vector autoregressions · J. Mach. Learn. Res. 2015 |
Machine learning › Time series and sequential data › time series modeling
vector autoregressive model |
0.2 | 1 | 2015 | A direct estimation of high dimensional stationary vector autoregressions · J. Mach. Learn. Res. 2015 |
Mathematical optimization
high-dimensional statistics |
0.2 | 1 | 2015 | A direct estimation of high dimensional stationary vector autoregressions · J. Mach. Learn. Res. 2015 |
Mathematical optimization › regularization
regularized estimation |
0.2 | 1 | 2015 | A direct estimation of high dimensional stationary vector autoregressions · J. Mach. Learn. Res. 2015 |
Mathematical optimization
convex relaxation |
0.2 | 1 | 2014 | Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time · NIPS 2014 |
Mathematical optimization › convex relaxation
semidefinite relaxation |
0.2 | 1 | 2014 | Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time · NIPS 2014 |
Algorithms and data structures › numerical linear algebra › dimensionality reduction › principal component analysis
sparse principal component analysis |
0.2 | 1 | 2014 | Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time · NIPS 2014 |
Algorithms and data structures
polynomial-time algorithms |
0.1 | 1 | 2014 | Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time · NIPS 2014 |
Methods — techniques the papers use, named apart from their topics
tightening · 0.2convex relaxation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | A direct estimation of high dimensional stationary vector autoregressions
Huanran Lu, Han Liu 0001 |
J. Mach. Learn. Res. | 2 |
| 2014 | Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
Zhaoran Wang 0001, Huanran Lu, Han Liu 0001 |
NIPS | 2 |