PhD Dissertations-Machine Learning Department - Carnegie Mellon University

PhD Dissertations

[All are .pdf files]

Expressive Collaborative Music Performance via Machine Learning
Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations
Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum
Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators
Matt Wytock, 2016

Discovering Compact and Informative Structures through Data Partitioning
Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time
Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain
Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions
Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing
Ankur Parikh, 2015

Computational and Statistical Advances in Testing and Learning
Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain
Alona Fyshe, 2015

Learning Statistical Features of Scene Images
Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos
Bin Zhao, 2014

Statistical Text Analysis for Social Science
Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context
Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents
Prashant P. Reddy, 2013

On Learning from Collective Data
Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning
Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles
Liu Yang, 2013

Cortical spatiotemporal plasticity in visual category learning
Yang Xu, 2013

Short-Sighted Probabilistic Planning
Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms
Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning
Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems
Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications
Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning
Yucheng Low, 2013

Graph Structured Normal Means Inference
James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data
Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields
Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy
Rob Hall, 2013
(Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning
Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations
Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation
Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations
Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs
Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding
Yi Zhang, 2012

Target Sequence Clustering
Benjamin Shih, 2011

Nonparametric Learning in High Dimensions
Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications
Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs
Purnamrita Sarkar, 2010

Rare Category Analysis
Jingrui He, 2010

Coupled Semi-Supervised Learning
Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs
Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning
Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition
Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning
Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning
Hao Cen, 2009

Detecting Patterns of Anomalies
Kaustav Das, 2009

Dynamics of Large Networks
Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics
Jason Ernst, 2008

Stacked Graphical Learning
Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference
Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields
Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks
Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications
Leonid Kontorovich, 2007

Tools for Graph Mining
Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models
Ricardo Silva, 2005