Statistical Relational Learning at U Penn (2008)
Alexandrin Popescul, Dean P. Foster, Lyle H. Ungar
We do statistical relational learning by incrementally extracting data from a relational database, and computing features of that data which are then used in a classical discriminative statistical...
ABSTRACT Cluster-based Concept Invention for Statistical Relational Learning (2008)
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase...
Statistical Relational Learning at U Penn (2008)
Alexandrin Popescul, Dean P. Foster, Lyle H. Ungar
We do statistical relational learning by incrementally extracting data from a relational database, and computing features of that data which are then used in a classical discriminative statistical...
Alexandrin Popescul, Lyle H. Ungar
Automatically labeling document clusters with words which indicate their topics is difficult to do well. The most commonly used method, labeling with the most frequent words in the clusters, ends up...
Alexandrin Popescul, Steve Lawrence, Lyle H. Ungar, C. Lee Giles
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying...
Alexandrin Popescul, Dean P. Foster, Lyle H. Ungar
We do statistical relational learning by incrementally extracting data from a relational database, and computing features of that data which are then used in a classical discriminative statistical...
Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock
Abstract. Inductive logic programming (ILP) techniques are useful for analyzing data in multi-table relational databases. Learned rules can potentially discover relationships that are not obvious in...
Workshop on Learning Statistical Models from Relational Data, IJCAI-2003 (2007)
Statistical Relational Learning, Alexandrin Popescul, Dean P. Foster, Lyle H. Ungar
ide powerful modeling component but are often limited to a "flat" file propositional domain representation where potential features are fixed-size attribute vectors. Often the manual...
Cluster-based Concept Invention for Statistical Relational Learning (2004)
Popescul, Alexandrin, Ungar, Lyle H.
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase...
Statistical learning from relational databases (2004)
One fundamental limitation of classical statistical modeling is the assumption that data is represented by a single table, even though most real-world problem domains have complex relational...
Cluster-based Concept Invention for Statistical Relational Learning (2004)
Alexandrin Popescul, Lyle H. Ungar
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase...
Cluster-based concept invention for statistical relational learning (2004)
Alexandrin Popescul, Lyle H. Ungar
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves scalability through...
Statistical Relational Learning for Document Mining (2003)
Popescul, Alexandrin, Ungar, Lyle H, Lawrence, Steve, Pennock, David M.
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have complex relational...
Structural Logistic Regression for Link Analysis (2003)
Popescul, Alexandrin, Ungar, Lyle H.
We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational...
Statistical Relational Learning for Document Mining (2003)
Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock
A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most real-world databases have complex relational structures....
Structural Logistic Regression for Link Analysis (2003)
Alexandrin Popescul, Rin Popescul, Lyle H. Ungar
We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational...
Statistical Relational Learning for Document Mining (2003)
Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David Pennock
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have complex relational...
Statistical Relational Learning for Link Prediction (2003)
Alexandrin Popescul, Rin Popescul, Lyle H. Ungar
Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the...
Statistical Relational Learning for Document Mining (2003)
Alexandrin Popescul, Steve Lawrence
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have complex relational...
Methods and Metrics for Cold-Start Recommendations (2002)
Schein, Andrew I, Popescul, Alexandrin, Ungar, Lyle H, Pennock, David M
We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naive Bayes classifier on...
Towards Structural Logistic Regression: Combining Relational and Statistical Learning (2002)
Popescul, Alexandrin, Ungar, Lyle H, Lawrence, Steve, Pennock, David M
Inductive logic programming (ILP) techniques are useful for analyzing data in multi-table relational databases. Learned rules can potentially discover relationships that are not obvious in...
Mixtures of Conditional Maximum Entropy Models (2002)
Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar
Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation...
Popescul, Alexandrin, Ungar, Lyle H, Pennock, David M, Lawrence, Steve
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid...
PennAspect: Two-Way Aspect Model Implementation (2001)
Schein, Andrew I, Popescul, Alexandrin, Ungar, Lyle H
The two-way aspect model is a latent class statistical mixture model for performing soft clustering of co-occurrence data observations. It acts on data such as document/word pairs (words occurring in...
Clustering and Identifying Temporal Trends in Document Databases (2000)
Alexandrin Popescul, Gary Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying...