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SELECT Lab::About

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SELECT Lab::About | select.cs.cmu.edu Reviews

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1

SELECT Lab::Publications

http://select.cs.cmu.edu/publications/scripts/papers.cgi

Qiang Yan, XingXing Wang, Qiang Xu, Dongying Kong, Danny Bickson, Quan Yuan, and Qing Yang (2013). " Predicting Search Engine Switching in WSCD 2013 Challenge. Workshop on Web Search Click Data (WSCD). Siyuan Liu, Yisong Yue, and Ramayya Krishnan (2013). " Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). International Conference on Machine Learning (ICML). ACM SIGKDD Conference on Knowledge Discovery and Data...

2

IJCAI 2009 Computers and Thought Award Lecture by Carlos Guestrin

http://select.cs.cmu.edu/talks/ijcai-ct-2009/index.html

IJCAI 2009 Computers and Thought Award Lecture by Carlos Guestrin. How Optimized Environmental Sensing Helps Address Information Overload on the Web. In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: Where should we place the sensors in order to make effective and robust predictions?

3

SELECT Lab::About

http://select.cs.cmu.edu/index.html

Welcome to the Select Lab. Our main long-term research goal is developing efficient algorithms and methods for designing, analyzing, and controlling complex real-world systems. To achieve this goal, our research spans the entire spectrum from theoretical foundations to real-world applications. Algorithmically, we focus on machine learning. Our main applications are sensor networks, computer systems and robotics. Was awarded VMWare's graduate research fellowship. NIPS’11 Big Learning. The Big Learning wor...

4

submodularity.org: Tutorials, References, Activities and Tools for Submodular Optimization

http://select.cs.cmu.edu//tutorials/icml08submodularity.html

Is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-)optimal solutions. Tutorial on Submodularity in Machine Learning - New Directions. At ICML 2013 by Stefanie Jegelka and Andreas Krause [ pdf part 1. Tutorials on Submodularity in Machine Learning and Computer Vision. Invited tutorial Intellgent Optimization with Submodular Functions.

5

Large-Scale Machine Learning: Parallelism and Massive Datasets

http://select.cs.cmu.edu/meetings/biglearn09

Large-Scale Machine Learning: Parallelism and Massive Datasets. Friday December 11th from 7:30AM to 6:30PM. Hilton at Whistler in the Mt. Currie North Room. NIPS 2011 BigLearning Workshop! Bring together people with varying approaches to parallelism in machine learning to identify tools, techniques, and algorithmic ideas which have lead to successful parallel learning. Identify the next key challenges and opportunities to parallel learning. Prior NIPS workshops have focused on the topic of scaling machin...

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SELECT Lab::About

Welcome to the Select Lab. Our main long-term research goal is developing efficient algorithms and methods for designing, analyzing, and controlling complex real-world systems. To achieve this goal, our research spans the entire spectrum from theoretical foundations to real-world applications. Algorithmically, we focus on machine learning. Our main applications are sensor networks, computer systems and robotics. Was awarded VMWare's graduate research fellowship. NIPS’11 Big Learning. The Big Learning wor...

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