Here's a list of potential topics that had come up when we were brainstorming for this year.

Model selection

* When Bayesian inference shatters ....

DONE ** Gelman : DIC

DONE Mingjun: "BIC" WBIC" geometric infomration

DONE ** Structure learning: e.g., Koller and Friedman

Nonparametrics (Bayesian and otherwise)

* Bayesian nonparametric --- Nils Hjort edited book

All of nonparametric statistics

Gaussian processes

Learning with kernels

Inference

** Variational inference --- e.g., Kevin Murphy

Variational Inference in Nonconjugate Models

Nonparametric Bayes particle filtering

Optimisation

** Nesterov acceleration

* ADMM

Nesterov book

Statistics

** Asymptotic statistics --- van der Vaart

** Lasso, ARS, sparsity

Approximate Bayesian Computation

Scaling

* Bottou and Bousquet

* Papers on Vowpal Rabbit, Spark

* Several streaming NIPS papers

Information geometry

* Information geometry (ref from Jinli)

* Differential geomtery and statistics --- MK Murray

Machine learning

* Taskar stuff with DPPs

Good but I'm not sure of references

Something about stochastic approximation

Active learning

Transfer learning

Good if time

Recommender systems

[2] Casella & Robert --- MCMC Spectral clustering

-- CharlesSutton - 07 Nov 2013

Topic revision: r1 - 07 Nov 2013 - 14:13:46 - CharlesSutton
 
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