optiml.cs.princeton.edu

OptiML | Optimization and Machine Learning

Optimization and Machine Learning. The OptiML group at Princeton University conducts research in developing new machine learning models as well as faster and more efficient optimization techniques for training learning machines. The topics under which our research can be classified include. Convex Optimization with a focus on more efficient Stochastic Methods. Non-Convex Optimization: efficient algorithms with provable guarantees, extending the assumptions under which efficient optimization is possible.

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OptiML | Optimization and Machine Learning | optiml.cs.princeton.edu Reviews

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Optimization and Machine Learning. The OptiML group at Princeton University conducts research in developing new machine learning models as well as faster and more efficient optimization techniques for training learning machines. The topics under which our research can be classified include. Convex Optimization with a focus on more efficient Stochastic Methods. Non-Convex Optimization: efficient algorithms with provable guarantees, extending the assumptions under which efficient optimization is possible.

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OptiML | Optimization and Machine Learning | optiml.cs.princeton.edu Reviews

https://optiml.cs.princeton.edu

Optimization and Machine Learning. The OptiML group at Princeton University conducts research in developing new machine learning models as well as faster and more efficient optimization techniques for training learning machines. The topics under which our research can be classified include. Convex Optimization with a focus on more efficient Stochastic Methods. Non-Convex Optimization: efficient algorithms with provable guarantees, extending the assumptions under which efficient optimization is possible.

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optiml.cs.princeton.edu

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Uncategorized | OptiML

http://optiml.cs.princeton.edu/index.php/category/uncategorized

Optimization and Machine Learning. March 2, 2016. Welcome to WordPress. This is your first post. Edit or delete it, then start writing! Latest News from OptiML. Check out our new blog: minimizingregret.com. Containing a post on our Linear Time Second Order Stochastic Algorithm. About a year ago. Via Twitter Web Client. Our new paper LiSSA on Linear Time Stochastic Second Order Methods is online : arxiv.org/abs/1602.03943. About a year ago. Via Twitter Web Client. Proudly powered by WordPress.

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Links | OptiML

http://optiml.cs.princeton.edu/index.php/links

Optimization and Machine Learning. Latest News from OptiML. Check out our new blog: minimizingregret.com. Containing a post on our Linear Time Second Order Stochastic Algorithm. About a year ago. Via Twitter Web Client. Our new paper LiSSA on Linear Time Stochastic Second Order Methods is online : arxiv.org/abs/1602.03943. About a year ago. Via Twitter Web Client. Proudly powered by WordPress.

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People | OptiML

http://optiml.cs.princeton.edu/index.php/people

Optimization and Machine Learning. Latest News from OptiML. Check out our new blog: minimizingregret.com. Containing a post on our Linear Time Second Order Stochastic Algorithm. About a year ago. Via Twitter Web Client. Our new paper LiSSA on Linear Time Stochastic Second Order Methods is online : arxiv.org/abs/1602.03943. About a year ago. Via Twitter Web Client. Proudly powered by WordPress.

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Software | OptiML

http://optiml.cs.princeton.edu/index.php/software

Optimization and Machine Learning. Latest News from OptiML. Check out our new blog: minimizingregret.com. Containing a post on our Linear Time Second Order Stochastic Algorithm. About a year ago. Via Twitter Web Client. Our new paper LiSSA on Linear Time Stochastic Second Order Methods is online : arxiv.org/abs/1602.03943. About a year ago. Via Twitter Web Client. Proudly powered by WordPress.

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Publications | OptiML

http://optiml.cs.princeton.edu/index.php/publications

Optimization and Machine Learning. Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition. Zeyuan Allen-Zhu, Yuanzhi Li. Even Faster SVD Decomposition Yet Without Agonizing Pain. Zeyuan Allen-Zhu, Yuanzhi Li. Katyusha: The First Direct Acceleration of Stochastic Gradient Methods. Optimal Black-Box Reductions Between Optimization Objectives. Zeyuan Allen-Zhu, Elad Hazan. Second Order Stochastic Optimization in Linear Time. Naman Agarwal, Brian Bullins, Elad Hazan. 48th Annual Symposi...

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About this blog | Fly Fly Fly the Butterfly

https://zeyuan.wordpress.com/about-this-blog

Fly Fly Fly the Butterfly. This blog contains literature reviews related to my research, to results I am interested in, and more generally to whatever that can amaze me with the beautiful theory behind it. In 2016, after moving to Princeton University for a postdoc, I become obliged to write some blog posts for our OptiML. Group in the CS department. Using this as a turning point, I’d love to make my blog revive again! Fly Fly Fly the Butterfly. Create a free website or blog at WordPress.com.

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OptiML | Optimization and Machine Learning

Optimization and Machine Learning. The OptiML group at Princeton University conducts research in developing new machine learning models as well as faster and more efficient optimization techniques for training learning machines. The topics under which our research can be classified include. Convex Optimization with a focus on more efficient Stochastic Methods. Non-Convex Optimization: efficient algorithms with provable guarantees, extending the assumptions under which efficient optimization is possible.

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