Playlist: 29 videos

Learning, Algorithm Design and Beyond Worst-Case Analysis

This workshop will explore well-motivated non-worst-case approaches to the analysis of algorithms and problems, as well as to the development of techniques that can take advantage of underlying structure in instances. It will bring together...

0:48:13

Automatic Resource Bound Analysis and Linear Optimization

Jan Hoffmann, Carnegie Mellon University
https://simons.berkeley.edu/talks/jan-hoffmann-2016-11-15
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:52:18

Beyond Big-O: Statistical Analysis of Performance Scaling

Holger Hoos, University of British Columbia
https://simons.berkeley.edu/talks/holger-hoos-2016-11-15
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:43:6

Self-Improving Algorithms for Sorting and Geometric Problems

Ken Clarkson, IBM Almaden
https://simons.berkeley.edu/talks/ken-clarkson-2016-11-16
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:39:5

A PAC Approach to Application-Specific Algorithm Selection

Rishi Gupta, Stanford University
https://simons.berkeley.edu/talks/rishi-gupta-2016-11-16
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:32:37

Learning the Best Agorithm for Max-Cut, Clustering, and Other​ ​Partitioning Problems

Vaishnavh Nagarajan, Carnegie Mellon University
https://simons.berkeley.edu/talks/vaishnavh-nagarajan-2016-11-16
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:51:35

Learning as a Tool for Algorithm Design and Beyond-Worst-Case Analysis

Kevin Leyton-Brown, University of British Columbia
https://simons.berkeley.edu/talks/kevin-leyton-brown-2016-11-16
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:59:48

The Computational Benefit of Correlated Instances

Shafi Goldwasser, Massachusetts Institute of Technology
https://simons.berkeley.edu/talks/shafi-goldwasser-2016-11-16
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:43:0

Recovery Guarantee of Non-Negative Matrix Factorization via Alternating Updates

Yingyu Liang, Princeton University
https://simons.berkeley.edu/talks/yingyu-liang-2016-11-17
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:50:40

Characterizing the Typical Case Complexity of Formal Verification and Synthesis

Sanjit Seshia, UC Berkeley
https://simons.berkeley.edu/talks/sanjit-seshia-2016-11-17
Learning, Algorithm Design and Beyond Worst-Case Analysis
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0:43:12

Beyond Worst Case: When Complex Feedback Can Improve the Label Complexity of Active Learning

Kamalika Chaudhuri, UC San Diego
https://simons.berkeley.edu/talks/kamalika-chaudhuri-2016-11-17
Learning, Algorithm Design and Beyond Worst-Case Analysis
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