Dean P. Foster mainly focuses on Econometrics, Mathematical economics, Artificial intelligence, Statistics and Machine learning. Econometrics is closely attributed to Calibration in his work. His Best response, Nash equilibrium and Solution concept study in the realm of Mathematical economics interacts with subjects such as Learning rule.
His work on Semi-supervised learning and Fuzzy clustering as part of general Artificial intelligence study is frequently linked to Named-entity recognition, Similarity and Collaborative filtering, bridging the gap between disciplines. His study explores the link between Statistics and topics such as Feature selection that cross with problems in Model selection, g-prior and Decision theory. His Machine learning study incorporates themes from Word, Representation and Chunking, Natural language processing.
Statistics, Artificial intelligence, Econometrics, Algorithm and Mathematical optimization are his primary areas of study. His studies deal with areas such as Natural language processing, Machine learning and Pattern recognition as well as Artificial intelligence. His research is interdisciplinary, bridging the disciplines of Calibration and Econometrics.
His Algorithm research includes themes of Mixture model and Canonical correlation. The various areas that Dean P. Foster examines in his Mathematical optimization study include Regret, Selection and Applied mathematics. As a part of the same scientific study, Dean P. Foster usually deals with the Feature selection, concentrating on Linear regression and frequently concerns with Combinatorics.
Dean P. Foster mostly deals with Algorithm, Mathematical optimization, Artificial intelligence, Linear regression and Feature selection. His research integrates issues of Hidden Markov model and Canonical correlation in his study of Algorithm. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Calibration, Regret, Selection and Applied mathematics.
His research in Calibration intersects with topics in Contrast and Nash equilibrium. His Artificial intelligence research integrates issues from Machine learning, Pattern recognition and Natural language processing. His Regression study combines topics in areas such as Econometrics and Process.
Dean P. Foster mostly deals with Algorithm, Artificial intelligence, Canonical correlation, Scalability and Machine learning. The Algorithm study combines topics in areas such as Singular value, Latent variable and Mathematical optimization. Dean P. Foster works in the field of Mathematical optimization, focusing on Minimax in particular.
Dean P. Foster combines topics linked to Frame with his work on Artificial intelligence. His work deals with themes such as Dimension, Subspace topology, Matrix decomposition, Matrix and Linear complex structure, which intersect with Canonical correlation. His Probabilistic logic research extends to Machine learning, which is thematically connected.
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Stochastic Evolutionary Game Dynamics
Dean Foster;Peyton Young.
Theoretical Population Biology (1990)
Clustering Methods for Collaborative Filtering
Lyle H. Ungar;Dean P. Foster.
national conference on artificial intelligence (1998)
The risk inflation criterion for multiple regression
Dean P. Foster;Edward I. George.
Annals of Statistics (1994)
Calibration and empirical Bayes variable selection
Edward I George;Dean P Foster.
Continuous Record Asymptotics for Rolling Sample Variance Estimators
Dean P. Foster;Daniel B. Nelson.
Multi-View Learning of Word Embeddings via CCA
Paramveer Dhillon;Dean P Foster;Lyle H. Ungar.
neural information processing systems (2011)
A Spectral Algorithm for Latent Dirichlet Allocation
Animashree Anandkumar;Dean P. Foster;Daniel Hsu;Sham M. Kakade.
arXiv: Learning (2012)
Multi-view regression via canonical correlation analysis
Sham M. Kakade;Dean P. Foster.
conference on learning theory (2007)
Precision and Accuracy of Judgmental Estimation
Ilan Yaniv;Dean P. Foster.
Journal of Behavioral Decision Making (1997)
Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy
Dean P Foster;Robert A Stine.
Journal of the American Statistical Association (2004)
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