PIGlets Archive (2009)
Previous Meetings
- 11 May Athina: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 6 ("Convex Relaxations and Upper Bounds")
- 27 Apr Peter: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 6 ("Variational Methods in Parameter Estimation")
- 13 Apr Jyri: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 5 ("Mean Field Methods")
- 16 Mar Jakub: "Reversible Jump MCMC" (Peter J. Green and David I. Hastie)
- 16 Feb Ali: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), review and chapter 4 ("Sum-Product, Bethe-Kikuchi, and Expectation-Propagation")
- 02 Feb Andrew: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 4 ("Sum-Product, Bethe-Kikuchi, and Expectation-Propagation")
- 19 Jan Hannes: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 4 ("Sum-Product, Bethe-Kikuchi, and Expectation-Propagation")
- 24 Nov Nicolas: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 3 ("Exponential Families")
- 10 Nov Nicolas: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 3 ("Exponential Families")
- 20 Oct Mike: Modelling the spatiotemporal diffusion of Bicoid in the Drosophila
- 13 Oct Sebastian: "Graphical Models, Exponential Families, and Variational Inference" (M. J. Wainwright and M. I. Jordan), chapter 2 ("Introduction")
- 15 Sep Frank: Hybrid MCMC, chapter 11.5.2 from Bishop's Book
- 1 Sep Athina: sections 6&10 from Learning Deep Architectures for AI
- 21 Jul Kian Ming: Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
- 17 Mar Prospective PhD student talk
- 03 Mar Sebastian: More Gaussian Processes
- 17 Feb Sebastian: Gaussian Processes (chapters 1,2, and 4 from "Gaussian Processes")
- 03 Feb Kian Ming: Laplace's method approximations for probabilistic inference in belief networks with continuous variables, Choice of Basis for Laplace Approximation, Accurate Approximations for Posterior Moments and Marginal Densities
- 20 Jan Alex: MCMC; Neal's Tech Report, pages to read from it are: 1 ,60 , 87-89
- 04 Nov Book club "All of Statistics" (L. Wasserman), chapter 20 ("Nonparametric curve estimation")
- 14 Oct Book club "All of Statistics" (L. Wasserman), chapter 19 ("Log-Linear Models")
- 16 Sep Book club "All of Statistics" (L. Wasserman), chapters 15 / 16 ("Inference about Independence" / "Causal Inference")
- 19 Aug Book club "All of Statistics" (L. Wasserman), chapter 12 ("Statistical Decision Theory")
- 05 Aug Book club "All of Statistics" (L. Wasserman), chapter 11 ("Bayesian Inference")
- 22 July Book club "All of Statistics" (L. Wasserman), chapters 10-11 ("Hypothesis Testing and p-values" / "Bayesian Inference")
- 08 July Book club "All of Statistics" (L. Wasserman), chapters 9-10 ("Parametric Inference" / "Hypothesis Testing and p-values")
- 22 May Book club "All of Statistics" (L. Wasserman), chapters 7-8 ("Estimating the CDF and Statistical Functionals" / "The Bootstrap")
- 06 May Book club "All of Statistics" (L. Wasserman), chapters 6-7 ("Models, Statistical Inference and Learning" / "Estimating the CDF and Statistical Functionals")
- 22 Apr Book club "All of Statistics" (L. Wasserman), chapters 3-5
- 08 Apr Book club "All of Statistics" (L. Wasserman), chapters 1-3
- 06 Mar Lawrence, Nicolas: Variational Methods II. Bishop's book, chapter 10.4-10.6; Jordan et al. 1999: An Introduction to Variational Methods for Graphical Models; Jaakola 2000: Tutorial on variational approximation methods
- 19 Feb Hannes: Entropy estimation. Nemenman et al., 2004: Entropy and information in neural spike trains: Progress on the sampling problem; Nemenman et al., 2002: Entropy and inference, revisited
- 08 Feb Nicolas: Variational Methods
- 22 Jan Lawrence
- 20 Nov Kian Ming: Partial Least Squares. Overview and Recent Advances in Partial Least Squares; Additional Reference: A statistical framework for multivariate latent variable regression methods based on maximum likelihood
- 06 Nov Adrian: Duality between optimal control and optimal estimation. Todorov: Optimal control theory; Todorov: Revising and generalizing Kalman’s duality between optimal control and estimation
- 23 Oct Andrew: Clustering. Bayesian Agglomerative Clustering with Coalescents; A New Approach to Data Driven Clustering
- 09 Oct Nicolas: Deep Belief Networks. Hinton et al, 2006: A fast Learning Algorithm for Deep Belief Nets; other good reads: Hinton 2006: To recognize shapes, first learn to generate images; Bengio and LeCun , 2007: Scaling Learning Algorithms Towards AI
- 31 Jul Jon
- 17 Jul Sebastian: Expectation Propagation
- 03 Jul Stefan: Introduction to Statistical Learning Theory
- 08 May Stefan: Introduction to Statistical Learning Theory
- 24 Apr Adrian: Fokker-Planck equation and first-passage time problems: see Section 6.4 of Mark Van Rossum's Neural Computation lecture notes
- 10 Apr John and Lawrence: Stochastic Differential Equations
- 27 Mar Edwin: Kernel Methods
- 13 Mar Scott: Conditional Random Fields
- 27 Feb Edwin: GPs: Approximation methods for large datasets
- 13 Feb Moray: Gaussian Processes
Suggested Topics
Please add material that can be used as a text source for each of the topics. Material may include review papers, journal papers, notes, tutorials, book chapters, etc.
You can also add new thematic categories that will be interesting to the group, with or without material.
- Approximate Inference - Variational Methods (Loopy Belief Propagation, Expectation Propagation, etc)
- Chapter 10, Bishop's book
- M. I. Jordan et al., Machine Learning, vol.37, pp.183--233, (1999): An Introduction to Variational Methods for Graphical Models
- M. J. Wainwright and M. I. Jordan, Foundations and Trends in Machine Learning, vol.1, pp.1--305, (2008): Graphical Models, Exponential Families, and Variational Inference
- Monte Carlo Methods
- Hybrid MCMC: Bishop's book (Pattern Recognition and Machine Learning), chapter 11.5
- Gaussian Processes
- Bishop's book (Pattern Recognition and Machine Learning), chapter 6.4
- Dirichlet Processes
- Restricted Boltzmann Machines
- Statistical Learning Theory
- Active Learning
- Model Selection
- Probabilistic Relational Models
- Reinforcement Learning (MDPs, POMDPs, etc)
- Dimensionality Reduction - Manifold Learning
- Sequential Estimation (Kalman Filter Family, Particle Filters, etc)
- Optimization (Convex Optimization, etc)
- SEARN
- Structured Kernels
- Network Inference
- ODE and SDE Parameter Inference
- Energy based models
Suggested Books
Topic revision: r1 - 23 Jun 2010 - 15:44:52 - Main.s0570728