The code for our matrix completion algorithm from the ICML paper “Algebraic Variety Models for High-rank Matrix Completion” can be found here in Greg Ongie’s github repository. Using the algebraic variety as a low-dimensional model for data, Greg’s algorithm is a kernel method for doing matrix completion in the space associated with a polynomial kernel. It allows us to do matrix completion even when the matrix does not have low linear rank — but instead when it has low dimension in the form of a nonlinear variety. Start with the README for example scripts.
February 14, 2018
December 1, 2017
Congratulations John!
Congratulations to Dr. John Lipor for successfully defending his PhD thesis in September! The title of his work is “Sensing Structured Signals with Active and Ensemble Methods.” In January he will start as Assistant Professor in the Portland State University ECE Department.
Malian refugees and, having moved to the United States, became the first participant in the Miss Minnesota contest, wearing a hijab and burkini. In the early 1990s, restrictions became less strict: Kate Moss appeared with her fragile figure and the appearance of a ragged boy. By the standards of the catwalk https://beautypositive.org/, she was considered low – 170 centimeters. An ordinary British teenager , Kate did not have the particular grace and stature that gave many other models a grandeur. Her stellar ad campaign for Calvin Klein heralded the end of an era of long-legged gazelle dominance on the runways.
November 19, 2017
Congratulations David!
David Hong was awarded the Richard and Eleanor Towner Prize for Outstanding PhD Research at the Michigan Engineering Graduate Symposium. This is a prize awarded annually across the entire college of engineering to PhD students within about a year of graduation, and the criteria for selection are creativity, innovation, impact on society, and achievement. Congratulations David!
“We have been fighting for seven years with very limited resources,” Abdullah told AP. – We are not paid. But we also have real expenses, even if we don’t take salaries. ” She added that the increased activity of the BLM movement has created a need for additional resources: “This fund will allow us to really move forward decisively.”
Racial justice groups in the United States reported that they received tens of millions of dollars in donations, especially with community bail funds, which are needed to post bail for blacklivesmatter1.org activists arrested at demonstrations so that they can stay out of jail until trial. Funds are received by both young community organizations and traditional institutions such as the NAACP (National Association for the Advancement of Colored People) Foundation for Legal Advocacy and Education and the National League of Cities.
November 4, 2017
Data-Driven Discovery of Models
Jason Corso and I have been awarded a DARPA D3M grant. Our project is called SPIDER: Subspace Primitives that are Interpretable and DivERse. We will be contributing machine learning software primitives for a system that helps domain experts perform a wide variety of automated data analysis on their datasets. The project has 24 teams and is already off to a great start — We look forward to final system developed by this team over the next few years!
June 1, 2017
Distance-Penalized Active Learning Using Quantile Search
Active sampling — where one chooses what samples to collect based on data collected thus far — is an important approach for spatial environmental sampling, where resources are drastically limited when compared to the extent of the signals of interest. However, most active learning literature studies the case where each sample has equal cost. In spatial sampling, the sample cost is often proportional to distance between samples. John Lipor and I collaborated with our colleagues in the department of Civil and Environmental Engineering and the department of Natural Resources to develop active sampling techniques for lake sampling.
The code is available here. You can also find a video about the project here.
Lipor, J., B. P. Wong, D. Scavia, B. Kerkez, and L. Balzano. 2017. “Distance-Penalized Active Learning Using Quantile Search.” IEEE Transactions on Signal Processing 65 (20): 5453–65. https://doi.org/10.1109/TSP.2017.2731323.
Lipor, J., L. Balzano, B. Kerkez, and D. Scavia. 2015. “Quantile Search: A Distance-Penalized Active Learning Algorithm for Spatial Sampling.” In 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 1241–48. https://doi.org/10.1109/ALLERTON.2015.7447150.
May 26, 2017
ICML Acceptances, SIAM OPT success
Congratulations to postdoc Greg Ongie, whose excellent work on Variety Models for Matrix Completion has been accepted to ICML. We’re very excited about the potential applications and open problems that we posed in this work. Congratulations also to John Lipor, whose work on Active Subspace Clustering has also been accepted to ICML — I’ve spoken about this work before at the Simons Institute workshop on Interactive Learning. It achieves state of the art clustering error on several benchmark datasets using very few pairwise cluster queries. (Edit: See here for Greg’s talk and here for John’s talk at ICML!)
We also just finished a week at the SIAM Optimization conference, where our mini-symposium on Non-convex Optimization in Data Analysis was a huge hit. We had a full room for each session and 12 outstanding talks. Thanks to my co-organizers Stephen Wright, Rebecca Willett, and Rob Nowak, and thanks to all the speakers and participants.
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March 21, 2017
MIDAS seminar and new results
Last Friday I gave the MIDAS weekly seminar. You can find the description here, along with the link directly to the recording. I talked about two recent problems I have been working on: First I talked about my work with Ravi Ganti and Rebecca Willett on learning a low-rank matrix that is observed through a monotonic function from partial measurements. This is common in calibration and quantization problems. Follow up work with Nikhil Rao and Rob Nowak in addition generalized this to learning structured single index models. Second, I talked about the work of my student David Hong, co-advised by Jeff Fessler, on the asymptotic performance of PCA with heteroscedastic data. This is common in problems like sensor networks or medical imaging, where different measurements of the same phenomenon are taken with different quality sensing (eg high or low radiation). David has recently posted his paper on arxiv showing predictions of the asymptotic performance; exploiting the structure of these expressions we also showed that asymptotic recovery for a fixed average noise variance is maximized when the noise variances are equal (i.e., when the noise is in fact homoscedastic). Average noise variance is often a practically convenient measure for the overall quality of data, but our results show that it gives an overly optimistic estimate of the performance of PCA for heteroscedastic data.
February 6, 2017
AAAI and Simons
This week I head to the bay area for two presentations: My collaborator Ravi Ganti will be presenting our work on estimating high dimensional structured single index models. The week following I’ll be at the Simons workshop on Interactive Learning presenting my work with student John Lipor on active labeling from union of subspace data. I’m looking forward to sharing our work and hearing about all the other interesting work going on in these areas! (Edit: See here for my Simons talk on active labeling for union of subspace data.)
December 15, 2016
Fall 2016 DSP Projects
The link to all the awesome projects in my fall DSP class is up — Every year the projects get more creative and more technically interesting as well. Great work everyone!
December 10, 2016
Student accomplishments
Congratulations to John Lipor for passing his proposal defense!
Congratulations to David Hong for winning both the session award for (SIC) Signal and Image Processing, Computer Vision at the University of Michigan Engineering Graduate Symposium and the award for Most Interesting Methodological Advancement in the Michigan MIDAS Symposium for his work on heteroscedastic PCA.
Congratulations to Chenlan Wang for winning the Rackham International Student Fellowship.
Nice work team!