Ph.D. Dissertations - Michael Jordan

Multiple Optimality Guarantees in Statistical Learning
John Duchi [2014]

Safety, Risk Awareness and Exploration in Reinforcement Learning
Teodor Moldovan [2014]

Learning from Subsampled Data: Active and Randomized Strategies
Fabian Wauthier [2013]

Matrix Factorization and Matrix Concentration
Lester Mackey [2012]

Randomized Algorithms for Scalable Machine Learning
Ariel Jacob Kleiner [2012]

Bayesian Nonparametric Latent Feature Models
Kurt Miller [2011]

Incorporating Supervision for Visual Recognition and Segmentation
Alex Yu Jen Shyr [2011]

Learning Dependency-Based Compositional Semantics
Percy Shuo Liang [2011]

Automating Datacenter Operations Using Machine Learning
Peter Bodik [2010]

Computational Methods for Meiotic Recombination Inference
Junming Yin [2010]

Modeling Events in Time Using Cascades Of Poisson Processes
Aleksandr Simma [2010]

Probabilistic Models of Evolution and Language Change
Alexandre Bouchard-Cote [2010]

Statistical models for analyzing human genetic variation
Sriram Sankararaman [2010]

Discriminative Machine Learning with Structure
Simon Lacoste-Julien [2009]

Nonparametric Bayesian Models for Machine Learning
Romain Jean Thibaux [2008]

Resampling Methods for Protein Structure Prediction
Benjamin Norman Blum [2008]

Learning in decentralized systems: A nonparametric approach
Xuanlong Nguyen [2007]

Predicting Protein Molecular Function
Barbara Elizabeth Engelhardt [2007]

A Kinetic Model for G protein-coupled Signal Transduction in Macrophage Cells
Patrick Joseph Flaherty [2006]

Automated Music Analysis Using Dynamic Graphical Models
Brian K. Vogel [2005]

Learning Blind Source Separation
Francis R. Bach [2005]

Statistical Software Debugging
Alice X. Zheng [2005]

Probabilistic Graphical Models and Algorithms for Genomic Analysis
Eric Poe Xing [2004]

Probabilistic Models for Text and Images
David M. Blei [2004]

Shaping and Policy Search in Reinforcement Learning
Andrew Y. Ng [2003]