Algorithms and Uncertainty Boot Camp

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source: Simons Institute    2016年8月22日
Algorithms and Uncertainty Boot Camp
The Boot Camp is intended to acquaint program participants with the key themes of the Algorithms and Uncertainty program. It will consist of mini-courses on five topics, each featuring two or more speakers and occupying one day, as follows:
Kamesh Munagala (Duke University): "Approximation Algorithms for Stochastic Optimization"
Matt Weinberg (Princeton University): "Sequential Decision Making: Prophets and Secretaries"
Seffi Naor (Technion Israel Institute of Technology): "Competitive Analysis of Online Algorithms"
Nikhil Bansal (Technische Universiteit Eindhoven) and Adam Wierman (Caltech): "Online Scheduling Meets Queueing"
Eli Upfal (Brown University): "Sample Complexity and Uniform Convergence"
Nicolò Cesa-Bianchi (University of Milan): "Online Learning and Online Convex Optimization"
Yishay Mansour (Tel Aviv University): "Reinforcement Learning and Markov Decision Processes"
Tim Roughgarden (Stanford University): "Beyond Worst-Case Analysis"
Kevin Leyton-Brown (University of British Columbia): "Understanding the Empirical Hardness of NP-Complete Problems"
Anna Karlin (University of Washington): "Uncertainty in Algorithmic Mechanism Design"
Eilyan Bitar (Cornell University) and Adam Wierman (Caltech): "Energy and Uncertainty"
For more information, please visit https://simons.berkeley.edu/workshops/uncertainty2016-boo...
These presentations were supported in part by an award from the Simons Foundation.

Approximation Algorithms for Stochastic Optimization I 1:08:35 Kamesh Munagala, Duke University https://simons.berkeley.edu/talks/kam...
Approximation Algorithms for Stochastic Optimization II 1:03:15
Sequential Decision Making: Prophets and Secretaries I 1:00:40
Sequential Decision Making: Prophets and Secretaries II 1:00:55
Competitive Analysis of Online Algorithms I 1:03:11
Competitive Analysis of Online Algorithms II 1:06:57
Online Scheduling Meets Queueing I 59:55
Online Scheduling Meets Queueing II 1:03:20
Sample Complexity and Uniform Convergence I 51:16
Sample Complexity and Uniform Convergence II 44:27
Online Learning and Online Convex Optimization I 44:15
Online Learning and Online Convex Optimization II 53:19
Reinforcement Learning and Markov Decision Processes I 44:07
Reinforcement Learning and Markov Decision Processes II 49:56
Beyond Worst-Case Analysis I 1:04:03
Beyond Worst-Case Analysis II 1:05:05
Understanding the Empirical Hardness of NP-Complete Problems I 58:05
Understanding the Empirical Hardness of NP-Complete Problems II 53:28
Uncertainty in Algorithmic Mechanism Design 56:36
Energy and Uncertainty I 1:06:03
Energy and Uncertainty II 1:00:19