Research Links

(Update: Feb 10 2012. Please you find broken links, please kindly drop me an email at sunjunus @ gmail.com)

Dedicated research websites of interest

• Compressive sensing 2.0 community (the universe of compressive sensing)
• Compressive sensing resources (Rice DSP)
• Low-rank matrix recovery and completion via convex optimization (Prof. Yi Ma’s research page)
• The similarity search wiki
• Optic Flow , Stereo, MView, MRF Evaluation (Middle-Bury)
• Deep Learning
• Kernel Machines (not quite up-to-date, mostly before 2009)
• Minimal Problems in Computer Vision (Geometric vision and algebraic equations)
• Gaussian Processes Website

Events/Workshops/Tutorials of interest

• CVPR Tutorials (2012, 2011, 2010, 2009, 2008, 2007, 2006)
• ICCV Tutorials (2011, 2009, 2007)
• ECCV Tutorials (2012, 2010, 2008, 2006)
• ICML Tutorials (2012, 2011, 2010, 2009, 2008, 2007, 2006)
• NIPS Tutorials (2012, 2011, 2010, 2009, 2008, 2007, 2006)
• STOC Programs (2011,  2010, 2009, 2008., 2007, 2006)
• FOCS Programs (2011, 2010, 2009, 2008, 2007, 2006)
• COLT Programs (2011, 2010, 2009, 2008, 2007, 2006)
• International Workshop on Optimization for Machine Learning (2011, 2010, 2009, 2008, in conjunction with NIPS).
• Workshop on Low-Rank Methods for Large-Scale Machine Learning. (2011, 2010, in conjunction with NIPS).
• Workshop on Discrete Optimization in Machine Learning. (2010, 2009, in conjunction with NIPS).
• Workshop on Transfer Learning via Rich Generative Models. (2010, in conjunction with NIPS).
• AAAI Fall Symposium on Manifold Learning and Its Applications (2010, 2009).
• SPARS Workshop: Signal Processing with Adaptive Sparse Structured Representations (2011, 2009, 2007, 2005).
• Duke Workshop on Sensing and Analysis of High-Dimensional Data (2011, 2009).

• Frontiers in Computer Vision (Organized by Alan Yuille and Aude Oliva, Aug 2011)
• Statistical Theory and Methods for Complex, High-Dimensional Data (Program at the Isaac Newton Institute for Mathematical Sciences, Cambridge U.)

• Tutorial on Compressive Sensing (Candes and others at LMS Invited Lecturer Series 2011, Cambridge U)
• SMALL workshop on sparse dictionary learning (Jan 2011).
• Modern trends in optimization and its application (Sep — Dec 2010, UCLA IPAM Long-term program)
• Machine learning summer school on theory and practice of computational learning (2009, Ohio State U, U. Chicago, TTI-C)
• 2nd International summer school on numerical linear algebra (2010, Gene Golub SIAM summer school 2010, Italy)
• Workshop on Algorithms for Modern Massive Data Sets (2012, 2010, 2008, 2006)
• Laplacian eigenvalues and eigenfunctions: theory, computation, application (UCLA IPAM Workshop, 2009)
• Prof. Yi Ma’s course on compressive sensing (UIUC, 2008 fall)
• Graph cuts and related discrete or continuous optimization problems (UCLA IPAM workshop, 2008).
• Compressive sensing and frontiers in signal processing (2007, UMN)
• 25 Years of RANSAC (2006, CVPR Workshop)
• Intelligent extraction of information from graphs and high dimensional data (UCLA IPAM Graduate Summer School, 2005)

Wonderful reference books and notes that are online

• Convex optimization (Boyd and Vandenberghe)
• Robust optimization (Ben-Tal, Ghaoui, Nemirovski)
• The elements of statistical learning: data mining, inference, and prediction (Hastie, Tibshirani and Friedman)
• Computer vision: algorithms and applications (Richard Szeliski)
• The quest for artificial intelligence: a history of ideas and achievements (Nils J. Nilsson)
• Artificial intelligence: Foundations of Computational Agents (David Poole and Alan Mackworth)
• Theoretical Foundation and Numerical Methods for Sparse Recovery (Ed. Fornasier, Massimo. Amongst the first volumes devoted to  the subject of compressive sensing. Individual parts kindly released by their respective authors)

1. Compressive Sensing and Structured Random Matrices (by Holger Rauhut)
2. Numerical Methods for Sparse Recovery (by Massimo Fornasier)
3. Sparse Recovery in Inverse Problems (by Ronny Ramlau and Gerd Teschke)
4. An Introduction to Total Variation for Image Analysis (by Antonin Chambolle etc)

• ** Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Michael Elad. ** if you have Springer Link access. Among the first volumes devoted to the subject of compressive sensing. )
• Probability: Theory and Examples (by Richard Durrent)
• Information Theory, Inference, and Learning Algorithms (by David Mackay)
• Gaussian Processes for Machine Learning (by Carl Edward Rasmussen and Christopher K. I. Williams)
• ** Foundations and Trends in Computer Graphics and Vision, Machine Learning, Robotics, Signal Processing, Communications and Information Theory, Theoretical Computer Science (Not open -access in general, but you can obtain most survey articles there via Google research… )
• Hand of Mathematical Functions (Abramowitz and Stegun)