Machine Learning and Data Science Research
Data plays a critical role in all areas of IEOR, from theoretical developments in optimization and stochastics to applications in automation, logistics, health care, energy, finance, and other areas. Much of the recent interest in data science and machine learning has been spurred by the growing ability to apply vast computational power to large scale datasets in nearly every application domain. Faculty and students in the UC Berkeley IEOR department are engaged in cutting edge and interdisciplinary research in ML/DS, including topics like developing scalable and memory-efficient learning algorithms, integrating prediction and optimization models, sparse learning models, addressing fairness concerns, reinforcement learning and control, clustering and learning with network data, as well as applications of ML/DS to various domains.
DART: Noise Injection for Robust Imitation Learning
Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg. “DART: Noise Injection for Robust Imitation Learning“. 1st Conference on Robot Learning (CoRL). Mt. View, CA. Nov 2017. Proceedings of Machine Learning Research volume 78
Smart “Predict, then Optimize”
Adam N. Elmachtoub, Paul Grigas. Smart “Predict, then Optimize”. Management Science, forthcoming. https://arxiv.org/abs/1710.08005
Sparse Computation for Large-Scale Data Mining
D.S. Hochbaum and P. Baumann. Sparse Computation for Large-Scale Data Mining. IEEE Transactions on Big Data, Vol 2, Issue 2, 151-174, 2016.
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Robert M. Freund, Paul Grigas, Rahul Mazumder. “A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives”. https://arxiv.org/abs/1505.04243v1.
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