Random Collection

1, Identifying relevant variables: PCA and ICA (nice, clear illustration juxtaposing PCA and ICA)

2, High-dimensional regression with unknown variance

3, Properties and applications of Fisher distribution on the rotation group

4, High-Dimensional Problems in Statistics

5, Statistical topology via Morse theory, persistence and nonparametric estimation

6, A statistical approach to persistent homology

7, Computational Methods in Applied Sciences

 

Machine Learning Reading Groups

1, Machine Learning Reading Group: Fall 2011 @ Purdue

JMLR

• Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs, Jim C. Huang, Brendan J. Frey, 12(Jan):301-348, 2011
• Forest Density Estimation, Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty, Larry Wasserman, 12(Mar):907–951, 2011
• The Indian Buffet Process: An Introduction and Review, Thomas L. Griffiths, Zoubin Ghahramani, 12(Apr):1185–1224, 2011
• Faster Algorithms for Max-Product Message-Passing, Julian J. McAuley, Tibério S. Caetano, 12(Apr):1349–1388, 2011
• Learning Latent Tree Graphical Models, Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky, 12(May):1771-1812, 2011
• Stochastic Methods for l1-regularized Loss Minimization, Shai Shalev-Shwartz, Ambuj Tewari 12(Jun):1865-1892, 2011

ICML-2011

• Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection, Abhimanyu Das and David Kempe
• Variational Heteroscedastic Gaussian Process Regression, Miguel Lazaro-Gredilla and Michalis Titsias
• Minimum Probability Flow Learning, Jascha Sohl-Dickstein, Peter Battaglino, and Michael DeWeese
• Dynamic Tree Block Coordinate Ascent, Daniel Turlow, Dhruv Batra, Pushmeet Kohli, Vladimir Kolmogorov
• Pruning nearest neighbor cluster trees, Samory Kpotufe, Ulrike von Luxburg
• On the Necessity of Irrelevant Variables, Dave Helmbold, Phil Long
• Risk-Based Generalizations of f-divergences, Darío García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez
• Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines, Jun Zhu, Ning Chen, Eric Xing
• Message Passing Algorithms for the Dirichlet Diffusion Tree, David Knowles, Jurgen Van Gael, Zoubin Ghahramani
• Tree preserving embedding, Albert Shieh, Tatsunori Hashimoto, Ado Airoldi
• Variational Inference for Stick-Breaking Beta Process Priors, John Paisley, Lawrence Carin, David Blei
• Infinite Dynamic Bayesian Networks, Finale Doshi, David Wingate, Josh Tenenbaum, Nicholas Roy
• Learning Recurrent Neural Networks with Hessian-Free Optimization, James Martens, Ilya Sutskever and Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey Hinton
• Probabilistic Matrix Addition, Amrudin Agovic, Arindam Banerjee, Snigdhansu Chatterje
• k-DPPs: Fixed-Size Determinantal Point Process, Alex Kulesza, Ben Taskar

UAI

• Sum-Product Networks: A New Deep Architecture, Hoifung Poon and Pedro Domingos
• Generalised Wishart Processes, Andrew Wilson and Zoubin Ghahramani
• A Sequence of Relaxation Constraining Hidden Variable Models, Greg Ver Steeg and Aram Galstyan
• Graph Cuts is a Max-Product Algorithm, Daniel Tarlow, Inmar Givoni, Richard Zemel and Brendan Frey

 

2, Machine Learning Reading Group, Fall 2011

University of Maryland, College Park

22 Sep	BV Chapters 1 and 4 (skimmed) and Optimization and ML (Sec 1, 2, 7, App)	Anshul Sawant
29 Sep	SW 1-25, Barzilai and Borwein, 1988, Nesterov 2009	–
06 Oct	SW 20-45, Nemirovski et al., 2009, Bertsekas 2009	–
13 Oct	Stochastic Meta Descent	–
20 Oct	Adaptive Subgradient Methods for Online Learning and Stochastic Optimization	–
27 Oct	Online convex programming and generalized infinitesimal gradient ascent	–
03 Nov	SVM Optimization: Inverse Dependence on Training Set Size	–
10 Nov	Ergodic Subgradient Descent	–
17 Nov	Incremental Proximal Methods (optional: Dictionary Learning)	–
01 Dec	Trace-Norm Regularization	–
08 Dec	The Concave-Convex Procedure

ICML 2011 and COLT 2011

Since I missed the whole summer, but during the summer, many interesting things happened, I have to make it up. So this post will be updated during the next few days. I will collect some posts from others here. I hope it would be helpful for you.

ICML 2011

COLT 2011

1, ICML 2011 and the future

2, Interesting Neural Network Papers at ICML 2011

3, Interesting papers at COLT 2011

4, The conference(s) post: ACL and ICML

5, 14th International Conference on Artificial Intelligence and Statistics 2011 –Ft.Lauderdale

6, KDD and MUCMD 2011