Demo Data from EDBT 2026 / reviewers in the wild / expert
Yau Shu Wong
dblp:29/5762
· DBLP profile ↗
5ranked-venue papers
3as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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.
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 75% Deep learning architectures and training · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
expectation-maximization |
0.1 | 1 | 2005 | Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation › expectation-maximization
monte carlo expectation maximization |
0.1 | 1 | 2005 | Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005 |
Machine learning › Deep learning architectures and training
state space model |
0.1 | 1 | 2005 | Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005 |
Machine learning › Probabilistic and Bayesian machine learning › dynamical system
switching state-space model |
0.1 | 1 | 2005 | Nested Monte Carlo EM Algorithm for Switching State-Space Models · IEEE Trans. Knowl. Data Eng. 2005 |
Methods — techniques the papers use, named apart from their topics
rao-blackwellization · 0.1gibbs sampler · 0.1Monte Carlo EM · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Nested Monte Carlo EM Algorithm for Switching State-Space ModelsabstractSwitching state-space models have been widely used in many applications arising from science, engineering, economic, and medical research. In this paper, we present a Monte Carlo Expectation Maximization (MCEM) algorithm for learning the parameters and classifying the states of a state-space model with a Markov switching. A stochastic implementation based on the Gibbs sampler is introduced in the expectation step of the MCEM algorithm. We study the asymptotic properties of the proposed algorithm, and we also describe how a nesting approach and the Rao-Blackwellized forms can be employed to accelerate the rate of convergence of the MCEM algorithm. Finally, the performance and the effectiveness of the proposed method are demonstrated by applications to simulated and physiological experimental data. Cristina Adela Popescu, Yau Shu Wong |
IEEE Trans. Knowl. Data Eng. | 2 |
| 2004 | Monte Carlo Approach for Switching State-Space Models
Cristina Adela Popescu, Yau Shu Wong |
IEA/AIE | 2 |
| 2001 | Parameter extraction by parallel neural networks
Yau Shu Wong, Ben H. K. Lee, T. K. S. Wong |
Intell. Data Anal. | 1 |
| 1989 | Approximate polynomial preconditionings applied to biharmonic equations
Yau Shu Wong |
J. Supercomput. | 1 |
| 1988 | Solving large elliptic difference equations on CYBER 205
Yau Shu Wong |
Parallel Comput. | 1 |