PPT-Randomized Algorithms for Cuts and

Colouring David Pritchard NSERC Postdoctoral Fellow What Can Randomness Do Part 1 Check 3edgeconnectivity in a distributed network Joint with Ramakrishna Thurimella Denver Part 2 Find many disjoint set. Some Machine Learning algorithms require a discrete feature space but in realworld applications con tinuous attributes must be handled To deal with this problem many supervised discretization meth ods have been proposed but little has been done to s Simon Prince. s.prince@cs.ucl.ac.uk. Plan of Talk. Denoising. problem. Markov random fields (MRFs). Max-flow / min-cut. Binary MRFs (exact solution). Binary . Denoising. Before. After. Image represented as binary discrete variables. Some proportion of pixels randomly changed polarity.. CS648. . Lecture 3. Two fundamental problems. Balls into bins. Randomized Quick Sort. Random Variable and Expected . value. 1. Balls into BINS. Calculating probability of some interesting events. 2. CS648. . Lecture 15. Randomized Incremental Construction . (building the background). 1. Partition Theorem. A set of events . ,…,. . defined over a probability space (. ,. P. ) is said to induce a partition of . CS648. . Lecture 6. Reviewing the last 3 lectures. Application of Fingerprinting Techniques. 1-dimensional Pattern matching. . Preparation for the next lecture.. . 1. Randomized Algorithms . discussed till now. CS648. . Lecture 17. Miscellaneous applications of . Backward analysis. 1. Minimum spanning tree. 2. Minimum spanning tree. . 3. b. a. c. d. h. x. y. u. v. 18. 7. 1. 19. 22. 10. 3. 12. 3. 15. 11. 5. CS648. . Lecture . 25. Derandomization. using conditional expectation. A probability gem. 1. Derandomization. using . conditional expectation. 2. Problem 1. : Large cut in a graph. Problem:. Let . CS648. . Lecture 4. Linearity of Expectation with applications. (Most important tool for analyzing randomized algorithms). 1. RECAP from the last lecture. 2. Random variable. Definition. :. . A random variable defined over a probability space (. Authorized licensed use limited to: Univ of Texas at Dallas. Downloaded on December 2, 2009 at 22:08 from IEEE Xplore. Restrictions apply. possible instance problem, and would call that the (worst-c 1-2 interactive . lessons . - with teacher notes and resources. Appropriate for any age group or subject class - . i.e. . Junior Technology, Senior Hospitality. Brunoise. This is a very small diced cube, sized between 1-3 mm square. . . Lecture 2. Randomized Algorithm for Approximate Median. Elementary Probability theory. 1. Randomized Monte Carlo . Algorithm for. . approximate median . 2. This lecture was delivered at slow pace and its flavor was that of a tutorial. . Lower Bounds, and Pseudorandomness. Igor Carboni Oliveira. University of Oxford. Joint work with . Rahul Santhanam. (Oxford). 2. Minor algorithmic improvements imply lower bounds (Williams, 2010).. NEXP. Most people buy their meat in the form of cuts, joints or mince. Meat is also bought ready prepared, e.g. sausages, ham, burgers, kebabs.. Knowing where meat comes helps you know how to prepare, cook and serve it.. Wendy . Parulekar. MD, FRCP(C). Wei Tu PhD. Objectives. To review the classification of randomized phase II trial designs. To propose and critique potential  randomized phase II trials designs for a concept in head and neck cancer (case scenario to be presented).