VLDB 2026 Research / reviewers in the wild / expert
Robert M. Fung
dblp:66/2137
· DBLP profile ↗
11ranked-venue papers
5as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 9 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.
| Databases, data mining, and information retrieval
2 papers |
Information retrieval · 74% Data mining · 26% | |
| Artificial intelligence
2 papers |
Probabilistic and Bayesian machine learning · 52% Efficient and distributed learning · 24% Graph learning · 24% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › predictive modeling › classification
decision tree learning |
0.0 | 1 | 1991 | Classification Trees for Information Retrieval · ML 1991 |
Information retrieval › document retrieval
concept-based retrieval |
0.0 | 1 | 1990 | An Architecture for Probabilistic Concept-Based Information Retrieval · SIGIR 1990 |
Information retrieval › retrieval models
probabilistic retrieval model |
0.0 | 1 | 1990 | An Architecture for Probabilistic Concept-Based Information Retrieval · SIGIR 1990 |
Information retrieval
retrieval models |
0.0 | 1 | 1990 | An Architecture for Probabilistic Concept-Based Information Retrieval · SIGIR 1990 |
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference
bayesian network inference |
0.0 | 1 | 1989 | Node Aggregation for Distributed Inference in Bayesian Networks · IJCAI 1989 |
Machine learning › Efficient and distributed learning
distributed inference |
0.0 | 1 | 1989 | Node Aggregation for Distributed Inference in Bayesian Networks · IJCAI 1989 |
Machine learning › Graph learning
node aggregation |
0.0 | 1 | 1989 | Node Aggregation for Distributed Inference in Bayesian Networks · IJCAI 1989 |
Information retrieval
indexing |
0.0 | 1 | 1990 | An Architecture for Probabilistic Concept-Based Information Retrieval · SIGIR 1990 |
Methods — techniques the papers use, named apart from their topics
probabilistic modeling · 0.0probabilistic model induction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Symbolic probabilistic inference with both discrete and continuous variablesabstractThe importance of resolving general queries in Bayesian networks using the symbolic probabilistic inference (SPI) algorithm is considered. SPI applies the concept of dependency-directed backward search to probabilistic inference, and is incremental with respect to both queries and observations. Unlike traditional Bayesian network inferencing algorithms, the SPI algorithm is goal directed, performing only those calculations that are required to respond to queries. Research to date on SPI applies to Bayesian networks with only discrete-valued variables or only continuous variables (linear Gaussian) and does not address networks with both discrete and continuous variables. In this paper, we extend the SPI algorithm to handle Bayesian networks made up of both discrete and continuous variables (SPI-DC). The only topological constraint of the networks is that the successors of any continuous variable have to be continuous variables as well. In order to have exact analytical solution, the relationships between the continuous variables are restricted to be "linear Gaussian." With new representation, SPI-DC modifies the three basic SPI operations: multiplication, summation, and substitution. However, SPI-DC retains the framework of the SPI algorithm, namely building the search tree and recursive query mechanism and therefore retains the goal-directed and incrementality features of SPI.> Kuo-Chu Chang, Robert M. Fung |
IEEE Trans. Syst. Man Cybern. | 2 |
| 1994 | Backward Simulation in Bayesian Networks
Robert M. Fung, Brendan Del Favero |
UAI | 1 |
| 1991 | Classification Trees for Information Retrieval
Stuart L. Crawford, Robert M. Fung, Lee A. Appelbaum, Richard M. Tong |
ML | 2 |
| 1991 | Symbolic Probabilistic Inference with Continuous Variables
Kuo-Chu Chang, Robert M. Fung |
UAI | 2 |
| 1991 | Symbolic Probabilistic Inference with Evidence Potential
Kuo-Chu Chang, Robert M. Fung |
UAI | 2 |
| 1990 | Constructor: A System for the Induction of Probabilistic Models
Robert M. Fung, Stuart L. Crawford |
AAAI | 1 |
| 1990 | An Architecture for Probabilistic Concept-Based Information RetrievalabstractArticle An architecture for probabilistic concept-based information retrieval Share on Authors: R. M. Fung Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , S. L. Crawford Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , L. A. Appelbaum Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile , R. M. Tong Advanced Decision Systems, 1500 Plymouth Street, Mountain View, California Advanced Decision Systems, 1500 Plymouth Street, Mountain View, CaliforniaView Profile Authors Info & Claims SIGIR '90: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrievalDecember 1989 Pages 455–467https://doi.org/10.1145/96749.98252Online:01 December 1989Publication History 19citation520DownloadsMetricsTotal Citations19Total Downloads520Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access Robert M. Fung, Stuart L. Crawford, Lee A. Appelbaum, Richard M. Tong |
SIGIR | 1 |
| 1990 | Refinement and coarsening of Bayesian networks
Kuo-Chu Chang, Robert M. Fung |
UAI | 2 |
| 1989 | Node Aggregation for Distributed Inference in Bayesian Networks
Kuo-Chu Chang, Robert M. Fung |
IJCAI | 2 |
| 1989 | Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
Robert M. Fung, Kuo-Chu Chang |
UAI | 1 |
| 1985 | Metaprobability and Dempster-Shafer in Evidential Reasoning
Robert M. Fung, Chee Yee Chong |
UAI | 1 |