When allocating objects among people with different preferences, two major goals are Pareto efficiency and fairness. Since the objects are indivisible, there may not exist any fair allocation. For example, when there is a single house and two people, every allocation of the house will be unfair to one person. Therefore, several common approximations have been studied, such as maximin-share fairness (MMS), envy-freeness up to one item (EF1), proportionality up to one item (PROP1), and equitability up to one item (EQ1). The problem of efficient approximately-fair item allocation is to find an allocation that is both Pareto-efficient (PE) and satisfies one of these fairness notions. The problem was first presented at 2016 and has attracted considerable attention since then. (Wikipedia).
This video introduced fair division. Site: http://mathispower4u.com
From playlist Fair Division
Unit 5 - pareto optimal allocations part 3
From playlist Courses and Series
Unit 5 - practice problem 1 question
From playlist Courses and Series
Unit 5 - pareto optimal allocations part 2
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Unit 5 - pareto optimal allocations part 5
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Unit 5 - pareto optimal allocations part 4
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Christos Kalaitzis: Approximating the Maximum Budgeted Allocation Problem using the Configuration LP
The Maximum Budgeted Allocation Problem is the problem of assigning indivisible items to agents, which have budget constraints, in order to maximize our total revenue. While the natural Assignment-LP for this problem is well-understood, and has an integrality gap of 3/4, the same is not tr
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
Unit 5 - pareto optimal allocations part 1
From playlist Courses and Series
Evaluation 3: effectiveness vs. efficiency
Search engines must be effective and efficient. An "effective" engine returns the results that are useful to its users (relevant results). An "efficient" engine responds to request quickly and does not require unrealistic computing resources. In this lecture we discuss how to measure both
From playlist IR13 Evaluating Search Engines
Intractability in Algorithmic Game Theory - Tim Roughgarden
Tim Roughgarden Stanford University March 11, 2013 We discuss three areas of algorithmic game theory that have grappled with intractability. The first is the complexity of computing game-theoretic equilibria, like Nash equilibria. There is an urgent need for new ideas on this topic, to ena
From playlist Mathematics
Hardness of Randomized Truthful Mechanisms for Combinatorial Auctions - Jan Vondrak
Jan Vondrak IBM Almaden March 26, 2012 The problem of combinatorial auctions is one of the basic questions in algorithmic mechanism design: how can we allocate/sell m items to n agents with private valuations of different combinations of items, so that the agents are motivated to reveal th
From playlist Mathematics
Overview and Recent Results in Combinatorial Auctions - Matt Weinberg
Computer Science/Discrete Mathematics Seminar II Topic: Overview and Recent Results in Combinatorial Auctions Speaker: Matt Weinberg Affiliation: Princeton University Date: February 7, 2023 In this talk, I'll first give a broad overview of the history of combinatorial auctions within TCS
From playlist Mathematics
Learning-Based Sketching Algorithms - Piotr Indyk
Seminar on Theoretical Machine Learning Topic: Learning-Based Sketching Algorithms Speaker: Piotr Indyk Affiliation: Massachusetts Institute of Technology Date: August 25, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Danny Perez - Scalable approaches to long-time atomistic dynamics: a journey to the exascale
Recorded 28 March 2023. Danny Perez of Los Alamos National Laboratory presents "Scalable approaches to long-time atomistic dynamics: a journey to the exascale" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing workshop. Abstract: Molecular d
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
Guy Rothblum - Individual Fairness - IPAM at UCLA
Recorded 11 July 2022. Guy Rothblum of Apple Inc. presents "Individual Fairness" at IPAM's Graduate Summer School on Algorithmic Fairness. Abstract: This session will focus on the techniques for achieving individual fairness. Learn more online at: http://www.ipam.ucla.edu/programs/summer-s
From playlist 2022 Graduate Summer School on Algorithmic Fairness
Apportionment: The Alabama Paradox
This video explains and provides an example of the Alabama paradox. Site: http://mathispower4u.com
From playlist Apportionment
Lecture 4 - Elementary data structures
This is Lecture 4 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Stony Brook University in 2007. The lecture slides are available at: http://www.cs.sunysb.edu/~algorith/video-lectures/2007/lecture4.pdf More informati
From playlist CSE373 - Analysis of Algorithms - 2007 SBU
Introduction to Algorithms - Problem Session 1: Asymptotic Behavior of Functions and Double-ended...
MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY Four examples of worked problems on the asymptotic behavior of functions
From playlist MIT 6.006 Introduction to Algorithms, Spring 2020
Unit 4 - social surplus part 1
From playlist Courses and Series