## About tianyizhou

Research Assistant at University Washington, Seattle,
Working on Machine Learning & Statistics in
MODE lab leaded by Prof. Carlos Guestrin, and
MELODI lab leaded by Prof. Jeff Bilmes.
Coresets for k-Segmentation of Streaming Data, NIPS 2014 Streaming Submodular Optimization: Massive Data Summarization on the Fly, KDD 2014 Advertisements

Many well-known machine learning methods aim to draw a line between two classes. However, in our recently accepted NIPS 2014 paper “Divide-and-Conquer Learning by Anchoring a Conical Hull“, we reduce lots of fundamental machine learning problems (a broad class of … Continue reading →

Our paper “Multi-task 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 24-27. Before that, let me introduce this new method. In summary, we tackle … Continue reading →

You can now download MATLAB code for NeSVM from here. In the code, options.mu is a key parameter to adjust the trade-off between consistent decreasing of primal object function, and the speed. So you need to roughly tune it to … Continue reading →

Here is the GreBsmo code for our AISTATS 2013 paper. You can use it as a greedy version of GoDec solver for X=L+S problem. It is much faster and more robust. There are three video subsequences you can play in … Continue reading →

Is it possible to finish a 60000×10000 matrix decomposition (NMF, PCA, etc) or completion in 6 seconds on your laptop’s matlab? Can we make it even faster by a simple distributable scheme? How to summarize a huge-scale dataset (ratings, movie, … Continue reading →

Prof. Geoff Webb, the Editor-in-Chief of Data Mining and Knowledge Discovery (Springer) announced in his kdnuggets website that our paper “Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction”, which was published on DMKD journal in 2011 and cited … Continue reading →

Our paper “Greedy Bilateral Sketch, Completion and Smoothing” has been accepted by AISIATS 2013. Abstracts reads below, PDF is here, and code will be coming soon. Abstract: Recovering a large low-rank matrix from highly corrupted, incomplete or sparse outlier overwhelmed … Continue reading →

We are just informed that our submission “Compressed Labeling (CL) on Distilled Labelsets (DL) for Multi-label 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 →

Here is a good news of GoDec (pertaining to our ICML 2011 paper): Semi-Soft GoDec is released. Different from the ordinary GoDec which imposes hard threshholding to both the singular values of the low-rank part L and the entries of the … Continue reading →