'Probability 1' diaporamas de présentation

Randomization in Graph Optimization Problems

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Faster algorithms for string matching problems: matching the convolution bound

Faster algorithms for string matching problems: matching the convolution bound. Piotr Indyk. ??? : ??? ??? ???. Outline. Introduction Randomized Boolean convolution Convolution over GF(2) in O(n)-time Application. Pattern matching. Input: two string t , p (text and patten)

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The parts and the whole: a. Collapse Theories b. Identical Constituents

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Extracting Mobility Statistics from Indexed Spatio-Temporal Datasets

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Some thoughts about the role of Randomness in algorithms

Some thoughts about the role of Randomness in algorithms. Guy. Using randomization in algorithms. Why add randomization to such an organized process as algorithms? History. We needed to select primes with say 512 bits but check that they are prime. The density of prime numbers is

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Truthful Mechanisms for Combinatorial Auctions with Subadditive Bidders

Truthful Mechanisms for Combinatorial Auctions with Subadditive Bidders. Speaker: Shahar Dobzinski Based on joint works with Noam Nisan & Michael Schapira. Combinatorial Auctions. m items, n bidders, each bidder i has a valuation function v i :2 M ->R + . Common assumptions:

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Quitting Games

Quitting Games. Eilon Solan , Tel Aviv University. SING 15, Turku; July 3, 2019. Stopping Games: Model. There are n players. In every stage each player decides whether to stop the game or to continue . If at least one player stops , the game terminates.

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MULTIPLICATION LAW. Let A and B be events and assume P(B) ? 0 . Then

(Stat49N - April 7, 2004). MULTIPLICATION LAW. Let A and B be events and assume P(B) ? 0 . Then P(A ? B) = P(A | B) P(B) The multiplication law is often useful in finding the probabilities of intersections, as the following examples illustrate.

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Online Learning for Online Pricing Problems

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Toward Optimal Utilization of Shared Random Access Channels

Toward Optimal Utilization of Shared Random Access Channels. Joseph (Seffi) Naor, Technion Danny Raz, Technion Gabriel Scalosub, University of Toronto. The Multiple Access Dilemma. 2 access points (APs), downlink traffic In each time slot, each AP transmits to a client

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Learning-based methods to MDP and POMDP (Reinforcement learning & Q-Learning)

Learning-based methods to MDP and POMDP (Reinforcement learning & Q-Learning). Presented by, Niketan Pansare ( np6@rice.edu ) Rice University. Outline. - Reinforcement Learning - Exploration-Exploitation tradeoff - Q-Learning and SARSA - Learning-based methods to POMDP

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Richard Cleve DC 3524 cleve@cs.uwaterloo Course web site at:

Introduction to Quantum Information Processing CS 467 / CS 667 Phys 467 / Phys 767 C&O 481 / C&O 681. Richard Cleve DC 3524 cleve@cs.uwaterloo.ca Course web site at: http://www.cs.uwaterloo.ca/~cleve/courses/cs467. Lecture 22 (2005). Contents. Recap: BB84 key exchange protocol

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A Crossover from the Universality of the Surface Roughenings with Random Relaxations

A Crossover from the Universality of the Surface Roughenings with Random Relaxations to Edwards-Wilkinson Universality. C.K.Lee and Yup Kim. Kyung-Hee Univ. DSRG. DSRG. v Abstract.

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PageRank

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Introduction to Probability (Dr. Monticino)

Introduction to Probability (Dr. Monticino). Assignment Sheet. Read Chapters 13 and 14 Assignment #8 (Due Wednesday March 23 rd ) Chapter 13 Exercise Set A: 1-5; Exercise Set B: 1-3 Exercise Set C: 1-4,7; Exercise Set D: 1,3,4 Review Exercises: 2,3, 4,5,7,8,9,11 Chapter 14

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C-DEM: A Multi-Modal Query System for Drosophila Embryo Databases

C-DEM: A Multi-Modal Query System for Drosophila Embryo Databases. Fan Guo , Lei Li, Eric Xing, Christos Faloutsos Carnegie Mellon University {fanguo, leili, epxing, christos@cs.cmu.edu}. http://www.db.cs.cmu.edu:8080/cdem/demo.html. Background. Fruit-fly development in genetic study:

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MATH 5440 / EDUC 5838

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FISHY PROBABILITY

FISHY PROBABILITY. AIM: Understand how to express probability as a fraction. J. What is the probability of catching a goldfish?. P(Goldfish)=. 1 4. J. What is the probability of catching a blue fish?. P(Blue fish)=. 3 4. J. What is the probability of catching a goldfish?. 3 6. 1 2.

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Quantum and probabilistic finite multitape automata

Quantum and probabilistic finite multitape automata. Ginta Garkaje and Rusins Freivalds Riga, Latvia. First, we discuss the following 2-tape language L 1 = {(0 n 1 m ,2 k )| n=m=k }.

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