'Convex optimization' diaporamas de présentation

Statistical Learning

Statistical Learning. Dong Liu Dept. EEIS, USTC. Chapter 1. Linear Regression. From one to two Regularization Basis functions Bias-variance decomposition Different regularization forms Bayesian approach. A motivating example 1/2. What is the height of Mount Qomolangma ?

1.52k views • 79 slides

Journey to New Areas of Optimization Trends in Modern Optimization

Journey to New Areas of Optimization Trends in Modern Optimization. Feb., 2007 Kyungchul Park. Contents. Modern Convex Optimization From LP to Linear Cone Programming Robust Optimization Handling Uncertainty on Data Q&A. Journey I Modern Convex Optimization. Optimization as a Tool.

695 views • 48 slides

A Research Program in Reliable Adaptive Distributed Systems (RADS)

A Research Program in Reliable Adaptive Distributed Systems (RADS). Armando Fox Stanford University Michael Jordan, Randy Katz, George Necula, David Patterson, Doug Tygar University of California, Berkeley. Presentation Outline. A New Vision for Networked Systems

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Applications of Light Field and Computational Photography

585 views • 41 slides

Linear Algebra Review (with a Small Dose of Optimization)

Linear Algebra Review (with a Small Dose of Optimization). Hristo Paskov CS246. Outline. Basic definitions Subspaces and Dimensionality Matrix functions: inverses and eigenvalue decompositions Convex optimization. Vectors and Matrices. Vector May also write. Vectors and Matrices.

539 views • 39 slides

15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu

Artificial Intelligence: Representation and Problem Solving Optimization (1): Optimization and Convex Optimization. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Logistics. Complete the CMU 'course hours worked' survey. Recap.

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MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley )

Convex Optimization in Machine Learning. MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley.edu ) L. El Ghaoui, M. Jordan, C. Bhattacharrya, N. Cristianini, P. Bartlett U.C. Berkeley. Convex Optimization in Machine Learning. Advanced Convex Optimization in Machine Learning. SDP.

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MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley )

Convex Optimization in Machine Learning. MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley.edu ) L. El Ghaoui, M. Jordan, C. Bhattacharrya, N. Cristianini, P. Bartlett U.C. Berkeley. Convex Optimization in Machine Learning. Advanced Convex Optimization in Machine Learning. SDP.

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Exact or stable image\signal reconstruction from incomplete information

Exact or stable imagesignal reconstruction from incomplete information. Project guide: Dr. Pradeep Sen UNM (Abq). Submitted by: Nitesh Agarwal IIT Roorkee (India). Images and Signals. Digital Images. (figures from matlab). Digital Signals (Discrete). After Sampling.

394 views • 28 slides

Simulated annealing for convex optimization

Simulated annealing for convex optimization. Adam ? . Kalai: TTI-Chicago Santosh Vempala: MIT. Bar Ilan University 2004. 100-million dollar endowment (thanks, Toyoda!) 12 tenure -track slots, 18 visitors On University of Chicago campus Optional teaching Advising graduate students.

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Simulated annealing for convex optimization

Simulated annealing for convex optimization. Adam ? . Kalai: TTI-Chicago Santosh Vempala: MIT. Bar Ilan University 2004. 100-million dollar endowment (thanks, Toyoda!) 12 tenure -track slots, 18 visitors On University of Chicago campus Optional teaching Advising graduate students.

295 views • 28 slides

Support Vector Random Fields

Support Vector Random Fields. Chi-Hoon Lee, Russell Greiner, Mark Schmidt presenter: Mark Schmidt. Overview. Introduction Background Markov Random Fields ( MRFs ) Conditional Random Fields ( CRFs ) and Discriminative Random Fields ( DRFs ) Support Vector Machines ( SVMs )

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Efficient Sequential Decision-Making in Structured Problems

Efficient Sequential Decision-Making in Structured Problems. Adam Tauman Kalai Georgia Institute of Technology Weizmann Institute Toyota Technological Institute National Institute of Corrections. BANDITS AND REGRET. 8. 4. 3. 3. 1. 8. 1. 4. 6. 9. 8. 1. 2. 9. 5. 5. 8. 1.

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SE263 Video Analytics Course Project Initial Report

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Equalization of Modal Dispersion in Multimode Fiber Using Spatial Light Modulators

Equalization of Modal Dispersion in Multimode Fiber Using Spatial Light Modulators. Elad Alon, Vladimir Stojanovi ?, Joseph M. Kahn, Stephen Boyd, and Mark Horowitz. Multimode Fiber. Multimode is dominant type of fiber installed in current LANs

543 views • 21 slides

Client-Server Assignment for Internet Distributed Systems

Client-Server Assignment for Internet Distributed Systems. Overview. Introduction Problem Definition Problem Model Solution Conclusion. Introduction. Internet - Distributed System Example: Email,IMS. Features: 1 . Communication Load Clients assigned to two different servers.

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Boundary oracle and its use in convex optimization

Boundary oracle and its use in convex optimization. Boris Polyak Institute for Control Science Moscow Russia Luminy, April 2007. Contents. Convex optimization problem Boundary oracle Examples Center of gravity and Radon theorem Algorithm 1: Hit-and-Run

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Kinodynamic Motion Retiming for Humanoid Robots

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Online convex optimization Gradient descent without a gradient

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CSE203B Convex Optimization

CSE203B Convex Optimization. CK Cheng Dept. of Computer Science and Engineering University of California, San Diego. Outlines. Staff Instructor: CK Cheng, TA: Ariel Wang Logistics Websites, Textbooks, References, Grading Policy Classification History and Category Scope Coverage.

404 views • 11 slides