no code implementations • NeurIPS 2021 • Zeyu Shen, Lodewijk Gelauff, Ashish Goel, Aleksandra Korolova, Kamesh Munagala
We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion.
no code implementations • 27 Sep 2021 • Bryan C. Chan, Ashish Goel, Jonathan Kosh, Tyler G. R. Reid, Corey R. Snyder, Paul M. Tarantino, Saraswati Soedarmadji, Widyadewi Soedarmadji, Kevin Nelson, Feiqin Xie, Michael Vergalla
In recent decades, GNSS Radio Occultation soundings have proven an invaluable input to global weather forecasting.
no code implementations • 19 May 2020 • Tyler G. R. Reid, Bryan Chan, Ashish Goel, Kazuma Gunning, Brian Manning, Jerami Martin, Andrew Neish, Adrien Perkins, Paul Tarantino
Global Navigation Satellite Systems (GNSS) brought navigation to the masses.
no code implementations • 27 Mar 2020 • C. Seshadhri, Aneesh Sharma, Andrew Stolman, Ashish Goel
The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science.
no code implementations • 19 Jun 2019 • Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong, Ashish Goel
Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election correctly recovers the asymptotic outcome.
no code implementations • 12 Nov 2018 • Brandon Fain, Ashish Goel, Kamesh Munagala, Nina Prabhu
Constant sample complexity means that the mechanism (potentially randomized) only uses a constant number of ordinal queries regardless of the number of voters and alternatives.
1 code implementation • 2 Oct 2017 • Brandon Fain, Ashish Goel, Kamesh Munagala, Sukolsak Sakshuwong
In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus.
Computer Science and Game Theory Multiagent Systems
1 code implementation • 21 Jul 2015 • Peter Lofgren, Siddhartha Banerjee, Ashish Goel
First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators in experiments on a diverse set of networks.
no code implementations • 11 Jun 2012 • Reza Bosagh Zadeh, Ashish Goel
All of our results are provably independent of dimension, meaning apart from the initial cost of trivially reading in the data, all subsequent operations are independent of the dimension, thus the dimension can be very large.
no code implementations • 15 Jun 2010 • Bahman Bahmani, Abdur Chowdhury, Ashish Goel
We show that if we store $R>q\ln n$ random walks starting from every node for large enough constant $q$ (using the approach outlined for global PageRank), then the expected number of calls made to the distributed social network database is $O(k/(R^{(1-\alpha)/\alpha}))$.
1 code implementation • 18 Sep 2009 • Ashish Goel, Michael Kapralov, Sanjeev Khanna
Our techniques also give an algorithm that successively finds a matching in the support of a doubly stochastic matrix in expected time O(n\log^2 n) time, with O(m) pre-processing time; this gives a simple O(m+mn\log^2 n) time algorithm for finding the Birkhoff-von Neumann decomposition of a doubly stochastic matrix.
Data Structures and Algorithms Discrete Mathematics