HELLO, I'M
​Keyi Chen
​Keyi Chen
​Hi, I am currently a PhD student in Boston University, Computer Science Department, advised by Francesco Orabona​. Previously, I was at Stony Brook University.
My research focus on online learning, theoretical machine learning with special interest on parameter-free algorithms that does not require tuning learning rate.
Education
Publications
2018 - Now
Boston University – Boston, MA;
PhD; Computer Science
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Keyi Chen, Francesco Orabona, Generalized implicit follow-the-regularized-leader. ICML 2023
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Keyi Chen, Francesco Orabona, Implicit Interpretation of Importance Weight Aware Updates, ICML 2023 Workshop
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Keyi Chen, Ashok Cutkosky, Francesco Orabona, Implicit Parameter-free Online Learning with Truncated Linear Models, ALT 2022
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Keyi Chen, John Langford, Francesco Orabona, Better Parameter-free Stochastic Optimization with ODE updates for Coin-betting, AAAI 2022
2016–2018
Stony Brook University – Stony Brook, NY
MS; Statistics
Academic Service
2011–2015
Shandong University, Shandong, China
BS; Statistics
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Reviewer, AISTATS 2022/2023, AAAI 2023/2024​
Work Experience
Research Assistant, Optimal Lab at Boston University
Advisor: Francesco Orabona
​Jan.2019 - Now
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Designed algorithms for convex optimization, performed theoretical analysis, with special interest on online learning, and parameter-free algorithms which does not require tuning learning rate
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Implemented new algorithms and conduct comparison with baseline algorithms in Python/ Matlab to evaluate their empirical performance on classification/regression machine learning tasks
Machine Learning Intern, IQVIA
​Jun 2022 - Aug 2022
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Built machine learning models to predict personalized output in medical area tasks, extended the personalized input output hidden markov model (PIOHMM) to Bernoulli output
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Implemented a PIOHMM that captures personalized effect, and nurse effect, to predict the probability that the patient will engage in the future at specific period of time