STA 380

Basic Information

• Instructor: Mingyuan Zhou, Ph.D., Assistant Professor of Statistics
  
• Office: CBA 6.458 (Six floor, on the east side of the building that faces the entrance of Gregory Gym)
• Email: mingyuan.zhou@mccombs.utexas.edu
• Phone: 512-232-6763
• Website: http://mingyuanzhou.github.io/
• Office Hours: Thursday 4:00-5:30 PM. Welcome to come by my office at other times as well.
• Class time: Thursday 1:00 - 4:00 PM
• Classroom: CBA 6.420 (note the room change to accomodate more students; the classroom is located at the southwest corner of CBA six floor)

Syllabus

syllabus_STA380_Spring2018_ZHOU.pdf

Reading materials

• Christopher Bishop, Pattern Recognition and Machine Learning
• Kervin Murphy, Machine Learning: a Probabilistic Perspective
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning
• Lecture 1: Probability, Likelihood, Prior, and Posterior
  • C. Bishop, Chapters 1.2, 1.6, 2.1-2.4
  • K. Murphy, Chapter 2
• Lecture 2: Markov Chain Monte Carlo
  • C. Bishop, Chapters 11.2-11.4
  • K. Murphy, Chapters 23.1-23.4, 24
• Lecture 3: Variational Inference, Linear Regression
  • C. Bishop, Chapters 10.1-10.6, 3.1-3.3
  • K. Murphy, Chapters 21, 7
  • D. Blei, Variational Inference
  • Blei, Kucukelbir, & McAuliffe, Variational Inference: A Review for Statisticians
  • Bishop & Tipping, Variational Relevance Vector Machines
  • K. Murphy, Conjugate Bayesian Analysis of the Gaussian Distribution
• Lecture 4: Sparse Regression, Classification, Discrete Choice Analysis
  • C. Bishop, Chapters 4.3-4.5, 7.2
  • K. Murphy, Chapters 8, 9.4
  • M. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine
  • Park and Casella, The Bayesian Lasso
  • Polson & Scott, Data Augmentation for Support Vector Machines
  • Polson, Scott, & Windle, Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables
  • K. Train, Discrete Choice Methods with Simulation
• Lecture 5 & 6: Continuous Latent Variable Models, Bayesian Dicitonary Learning and Sparse Coding
  • C. Bishop, Chapter 12
  • K. Murphy, Chapters 12, 13
  • Goodfellow, Bengio, & Courville, Chapter 13
  • Tipping & Bishop, Probabilistic Principal Components Analysis
  • Salakhutdinov & Mnih, Bayesian Probabilistic Matrix Factorization using MCMC
  • Aharon, Elad, & Bruckstein, K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
  • Elad & Aharon, Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
  • Zhou et al., Non-parametric Bayesian Dictionary Learning for Sparse Image Representations
  • Chatper 3 of M. Zhou, Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
• Lecture 7 & 8: Discrete Data Analysis, Discrete Latent Variable Models, Topic Models, Mixed-Membership Models, Poisson Factor Analysis
  • K. Murphy, Chapters 3, 27
  • Blei, Ng, & Jordan, Latent Dirichlet Allocation
  • Hoffman, Blei, Wang, & Paisley, Stochastic Variational Inference
  • Griffiths & Steyvers, Finding Scientific Topics
  • Zhou, Hannah, Dunson, & Carin, Beta-Negative Binomial Process and Poisson Factor Analysis
  • Zhou & Carin, Negative Binomial Process Count and Mixture Modeling
  • Zhou & Scott, Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
• Lecture 9: Bayesian Models for Network Analysis, Stochastic Blockmodel, Edge Partition Model, Community Detection and Link Prediction
  • K. Murphy, Chapters 27.5-27.6
  • Newman & Girvan, Finding and Evaluating Community Structure in Networks
  • S. Fortunato, Community Detection in Graphs
  • Nowicki & Snijders, Estimation and Prediction for Stochastic Blockstructures
  • Kemp, Tenenbaum, Griffiths, Yamada, & Ueda. Learning Systems of Concepts with an Infinite Relational Model
  • Airoldi, Blei, Fienberg, & Xing, Mixed Membership Stochastic Blockmodels
  • P. Hoff, Modeling Homophily and Stochastic Equivalence in Symmetric Relational Data
  • Miller, Griffiths, & Jordan, Nonparametric Latent Feature Models for Link Prediction
  • Schmidt & Mørup, Nonparametric Bayesian Modeling of Complex Networks: An Introduction
  • Yang & Leskovec, Community-Affiliation Graph Model for Overlapping Network Community Detection
  • M. Zhou, Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
• Lecture 10 & 11: Bayesian Nonparametrics, Exchangeable Random Partitions, Completely Random Measures, Poisson Processes, Dirichlet Process, Chinese Restaurant Process, Gamma Process, Beta Process, Indian Buffet Process, Gamma/Beta-Negative Binomial Process
  • J.F.C. Kingman, Poisson Processes
  • Teh, Jordan, Beal, & Blei, Hierarchical Dirichlet Processes
  • Thibaux & Jordan, Hierarchical Beta Processes and the Indian Buffet Process
  • Griffiths & Ghahramani, The Indian Buffet Process: An Introduction and Review
  • Lijoi & Prunster, Models Beyond the Dirichlet Process
  • Zhou et al., Non-parametric Bayesian Dictionary Learning for Sparse Image Representations
  • Zhou, Favaro, & Walker, Frequency of Frequencies Distributions and Size Dependent Exchangeable Random Partitions
  • Zhou, Hannah, Dunson, & Carin, Beta-Negative Binomial Process and Poisson Factor Analysis
  • Zhou & Carin, Negative Binomial Process Count and Mixture Modeling
  • Zhou & Scott, Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
  • M. Zhou, Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling
  • M. Zhou, Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
• Lecture 12: Bayesian Deep Learning
  • Hinton, Osindero, & Teh, A Fast Learning Algorithm for Deep Belief Nets
  • LeCun, Bengio, & Hinton, Deep Learning
  • G. Hinton, Learning multiple layers of representation
  • Zhou, Cong, & Chen, Augmentable Gamma Belief Networks
  • Kingma & Welling, Auto-Encoding Variational Bayes
  • Rezende, Mohamed, & Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
  • Burda, Grosse, & Salakhutdinov, Importance Weighted Autoencoders
  • Rezende & Mohamed, Variational Inference with Normalizing Flows
  • Mnih & Gregor, Neural Variational Inference and Learning in Belief Networks
  • Maddison, Mnih, & Teh, The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
  • Jang, Gu, & Pool, Categorical Reparameterization with Gumbel-Softmax
  • Goodfellow et al., Generative Adversarial Nets
  • I. Goodfellow, Nips 2016 Tutorial: Generative Adversarial Networks
  • Donahue, Krähenbühl, & Darrell, Adversarial Feature Learning
  • Dumoulin et al., Adversarially Learned Inference
  • Mohamed & Lakshminarayanan, Learning in Implicit Generative Models
  • Tran, Ranganath, & Blei, Hierarchical Implicit Models and Likelihood-Free Variational Inference