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个人简介
I am interested in theoretical and computational aspect of Optimization, Machine Learning, Information Theory and High Dimensional Data Analytics.
My focus is to provide sufficient and necessary theoretical bounds on sample complexity and computational complexity for machine learning problems. We have been working on providing theoretical guarantees for combinatorial problems using continuous relaxation. For example, we have worked on learning Bayesian networks with low rank conditional probability tables. We are working on extending our approach beyond convex problems. As an example of that, we have successfully provided provable theoretical bounds for fair sparse regression problem using invex relaxation. Leveraging invexity to provide theoretical bounds is a novel approach for solving non-convex problems and we have many interesting results in this area.
研究兴趣
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CoRR (2023)
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2020 IEEE International Symposium on Information Theory (ISIT)pp.2486-2491, (2020)
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