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Recovery of Sparse Signals from a Mixture of Linear Samples
Soumyabrata Pal · Arya Mazumdar

Tue Jul 14 09:00 AM -- 09:45 AM & Tue Jul 14 10:00 PM -- 10:45 PM (PDT) @ Virtual #None

Mixture of linear regressions is a popular learning theoretic model that is used widely to represent heterogeneous data. In the simplest form, this model assumes that the labels are generated from either of two different linear models and mixed together. Recent works of Yin et al. and Krishnamurthy et al., 2019, focus on an experimental design setting of model recovery for this problem. It is assumed that the features can be designed and queried with to obtain their label. When queried, an oracle randomly selects one of the two different sparse linear models and generates a label accordingly. How many such oracle queries are needed to recover both of the models simultaneously? This question can also be thought of as a generalization of the well-known compressed sensing problem (Cand`es and Tao, 2005, Donoho, 2006). In this work we address this query complexity problem and provide efficient algorithms that improves on the previously best known results.

Author Information

Soumyabrata Pal (Umass Amherst)

I am a fourth-year Ph.D. student in the Computer Science Department (CICS) at the University of Massachusetts Amherst advised by Dr. Arya Mazumdar. Currently, I am a visiting scholar at the University of California, Berkeley. Prior to this I graduated from Indian Institute of Technology, Kharagpur in August 2016 with a Bachelor's degree in Electronics and Electrical Communication Engineering. My research interests are statistical machine learning, information theory and coding theory. More concisely, I love statistical recovery problems under different settings. Under this big umbrella, I have worked on problems related to random graph theory, high dimensional integration, semi-supervised clustering, trace reconstruction and parameter recovery in mixtures of standard distributions.

Arya Mazumdar (University of Massachusetts Amherst)

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