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Rong Ge
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I am broadly interested in theoretical computer science and machine learning. Modern machine learning algorithms such as deep learning try to automatically learn useful hidden representations of the data. How can we formalize hidden structures in the data, and how do we design efficient algorithms to find them? My research aims to answer these questions by studying problems that arise in analyzing text, images and other forms of data, using techniques such as non-convex optimization and tensor decompositions.
Papers79 papers
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ICML, pp.1768-1778, (2020)
NIPS 2020, (2020)
Mathematical Programming, pp.1-47, (2020)
ICML, pp.303-313, (2020)
arXiv: Probability, (2019)
ICLR, (2019)
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, no. 5-6 (2019): 393-536
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 14951-14962
SODA '19: Symposium on Discrete Algorithms
San Diego
California
January, 2019, (2019): 2755-2771
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 14574-14583
COLT, pp.3190-3193, (2019)
COLT, (2019): 727-757
COLT, pp.1394-1448, (2019)
arXiv: Learning, (2019)
arXiv: Learning, (2019)
ICML, pp.1466-1475, (2018)
international conference on learning representations, (2018)
arXiv: Learning, (2018)
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