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个人简介
My PhD research is to study various aspects of greedy algorithms in machine learning, both from theory and practical viewpoints. From the theory side, I have worked on studying approximation guarantees for "greedy-like" algorithms for sparsity and rank constrained problems for general functions based on their smoothness and convexity, and convergence rates for greedy algorithms like accelerated IHT, Matching Pursuit and boosting . From practical side, I have worked on using greedy selections for approximate variational inference for sparse regression, sparse PCA for fMRI applications, and for interpreting black box models. In the past I have worked on problems involving predicting buying propensity based on marketing touches, large scale recommendation systems, Ad Click prediction, and ranking.
研究兴趣
论文共 43 篇作者统计合作学者相似作者
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CoRR (2024)
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CoRR (2023)
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CoRR (2023): 4863-4880
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CoRR (2023)
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arxiv(2021)
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