February 2017 S M T W T F S « Sep 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Pages
 1bit measurements Bregman iteration classification clustering compressed sensing computer vision conical hull problem convex optimization dimension reduction DivideandConquer elastic net fast algorithm fast SVD feature selection fixed point continuation game greedy search group sparsity Hamming Compressed Sensing iterative thresholding Kmeans latent variable model lowrank manifold learning matrix completion matrix factorization multilabel learning Ncut Nesterov's method NIPS 2011 Nonnegative Matrix Factorization optimization Quantization recovery randomized optimization robust principal component analysis SDP Separable assumption sparse learning Spectral clustering structured learning SVM
ClustrMaps

What’s new
 List of Submodular Optimization on Streaming Data (In Update)
 DivideandConquer Learning by Anchoring a Conical Hull
 Multitask Copula – A semiparametric joint prediction model for multiple outputs with sparse graph structure
 NeSVM (Nesterov’s method for SVM) code for our ICDM 2010 paper
 AISTATS 2013 GreBsmo code is released
Articles
Tag Archives: structured learning
Multitask Copula – A semiparametric joint prediction model for multiple outputs with sparse graph structure
Our paper “Multitask Copula by Sparse Graph Regression“ has been accepted by KDD 2014 this year. So we can talk at the conference which is at NYC, between August 2427. Before that, let me introduce this new method. In summary, we tackle … Continue reading
Posted in Tianyi's work
Tagged fast algorithm, Hamming Compressed Sensing, structured learning
Leave a comment
Compressed Labeling: An important extension of Hamming Compressed Sensing; at NIPS now
We are just informed that our submission “Compressed Labeling (CL) on Distilled Labelsets (DL) for Multilabel Learning” is accepted by Machine Learning Journal (Springer). Online first PDF can be downloaded here. CL is an important application and extension of Hamming … Continue reading
SemiSoft GoDec: >4 times faster, autodetermined k
Here is a good news of GoDec (pertaining to our ICML 2011 paper): SemiSoft GoDec is released. Different from the ordinary GoDec which imposes hard threshholding to both the singular values of the lowrank part L and the entries of the … Continue reading
News about GoDec code and ICML 2011 paper
We recently published a google site for Go Decomposition (GoDec), presented on ICML 2011. Now you can find all the available information and upcoming news about GoDec on http://sites.google.com/site/godecomposition On the new site, there are 3 resources about GoDec we … Continue reading
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case
This paper is accepted by ICML 2011 for presentation. Now the final version is ready and can be downloaded from here GO. Abstract: Lowrank and sparse structures have been profoundly studied in matrix completion and compressed sensing. In this paper, we develop … Continue reading
Multilabel Learning via Structured Decomposition and Group Sparsity
This paper is now available on arxiv： http://arxiv.org/abs/1103.0102 In multilabel learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly … Continue reading