Lecture 17 Lecturer : Anna Karlin Scribe : Eric

Lecture 17 Lecturer : Ariel Procaccia Scribe : David Kurokawa 1 Setting and Model

To begin, we first rigorously define the model. The cake itself is represented by the real interval [0, 1] and a piece of this cake is a finite union of disjoint intervals X ⊆ [0, 1]. Furthermore, each agent i has a valuation function Vi that represents his/her assessment of pieces of the cake. That is, Vi(X) = x states that the piece X has value x to i. We will assume such Vi have the followin...

Cs294-1 On-line Computation & Network Algorithms Lecture 21: April 17

Spring 1997 Lecture 21: April 17 Lecturer: Yair Bartal Scribe: Tzu-Yi Chen This is the third of a series of lectures on probabilistic approximate metric spaces [Bartal96]. This lecture states a theorem relating probabilistic partitions to k-HST trees and then proves it. At the end of the lecture we brie y discuss why probabilistic approximate metric spaces are useful in practice. 21.1 Probabili...

Anna Bartkowiak NEURAL NETWORKS and PATTERN RECOGNITION Lecture notes Fall 2004 Institute of Computer Science , University of WrocÃlaw

Lecturer : David P . Williamson Scribe : Faisal Alkaabneh

Today we will look at a matrix analog of the standard scalar Chernoff bounds. This matrix analog will be used in the next lecture when we talk about graph sparsification. While we’re more interested in the application of the theorem than its proof, it’s still useful to see the similarities and the differences of moving from the proof of the result for scalars to the same result for matrices. Sc...

CSE 522 : Algorithms and Uncertainty Spring 2017 Lecture 10 — May 5 , 2017