Please also see my Google scholar profile; it is sometimes more up to date.
Preprints
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Thesis
Laura Balzano, Handling Missing Data in High-Dimensional Subspace Modeling, May 2012.
Bianca Dumitrascu, Boaz Nadler, and I hosted a virtual workshop in early September, supported by the Institute for Advanced Study. We had excellent speakers from across the spectrum of machine learning, statistics, and applications that consider missing data. You can find videos of all the seminars here.
I am excited that Amanda Bower will have the opportunity to discuss our new work in preference learning, “Preference Modeling with Context-Dependent Salient Features“, at ICML next week. In this work, we propose a new model for preference learning that takes into account the fact that when making pairwise comparisons, certain features may play an outside role in the comparison, making the pairwise comparison result inconsistent with a general preference order. Her video and the schedule for her “poster session availability” can be found here. We look forward to hearing people’s questions and feedback!
Kyle Gilman and I have a preprint out describing a new algorithm for online tensor completion and tracking. We derive and demonstrate an algorithm that operates on streaming tensor data, such as hyperspectral video collected over time, or chemo-sensing experiments in space and time. Kyle presented his work at the first fully virtual ICASSP, which you can view here. Anyone can register for free to this year’s virtual ICASSP and watch the videos, post questions, and join the discussion. Kyle’s code is also available here. We think this algorithm will have a major impact in speeding up low-rank tensor processing, especially with time-varying data, and we welcome questions and feedback.
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