Algorithms and Randomness Center (ARC)
Our mission is to identify problems with natural connections to algorithms and randomness. To help solve these problems and understand related phenomena by suggesting provable algorithms and algorithmic explanations. To formulate general tools based on the solutions and the insights behind them and thereby extend and solidify the theory of algorithms. To represent an algorithms and randomness think tank that scientists across campus can use as a resource.
All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved. Such materials may be used, quoted or reproduced for educational purposes only with prior permission, provided proper attribution is given. Any redistribution, reproduction or use of the materials, in whole or in part, is prohibited without prior permission of the author.
Collections in this community

ARC Talks and Events [84]
Distinguished lectures, colloquia, seminars and speakers of interest to the ARC community 
ARC Theory Day [5]
Thursday, November 10, 2011 and Friday, November 11, 2011 
ARC5 Distinguished Lecture [5]
Klaus Advanced Computing Building  August 28, 2012 
Modern Aspects of Submodularity [27]
March 1922, 2012 at Georgia Tech
Recent Submissions

Distribution testing: Classical and new paradigms
(20200302)One of the most fundamental problems in learning theory is to view input data as random samples from an unknown distribution and then to make statistical inferences about the underlying distribution. In this talk, we focus ... 
Improved Analysis of Higher Order Random Walks and Applications
(20200210)Local spectral expansion is a very useful method for arguing about the spectral properties of several random walk matrices over simplicial complexes. The motivation of this work is to extend this method to analyze the ... 
Spectral Independence in HighDimensional Expanders and Applications to the Hardcore Model
(20200127)We say a probability distribution µ is spectrally independent if an associated correlation matrix has a bounded largest eigenvalue for the distribution and all of its conditional distributions. We prove that if µ is ... 
Robust Mean Estimation in NearlyLinear Time
(20191202)Robust mean estimation is the following basic estimation question: given i.i.d. copies of a random vector X in ddimensional Euclidean space of which a small constant fraction are corrupted, how well can you estimate the ... 
Some new approaches to the heavy hitters problem
(20190916)In the 'frequent items' problem one sees a sequence of items in a stream (e.g. a stream of words coming into a search query engine like Google) and wants to report a small list of items containing all frequent items. In ... 
Fast Approximation Algorithms and Complexity Analysis for Design of Networked Systems
(20191118)This talk focuses on network design algorithms for optimizing average consensus dynamics, dynamics that are widely used for information diﬀusion and distributed coordination in networked control systems. Network design ... 
Rapidly Mixing Random Walks via LogConcave Polynomials (Part 2)
(20191106)(This is Part 2, continuation of Tuesday's lecture.) A fundamental tool used in sampling, counting, and inference problems is the Markov Chain Monte Carlo method, which uses random walks to solve computational problems. ... 
Rapidly Mixing Random Walks via LogConcave Polynomials (Part 1)
(20191105)A fundamental tool used in sampling, counting, and inference problems is the Markov Chain Monte Carlo method, which uses random walks to solve computational problems. The main parameter defining the efficiency of this ... 
Algorithmic Discrete Choice
(20191104)In this talk we consider random utility models for discrete choice. In discrete choice, the task is to select exactly one element from a discrete set of alternatives. We focus on algorithmic questions in this and related ... 
What 2layer neural nets can we optimize?
(20191028)Optimizing neural networks is a highly nonconvex problem, and even optimizing a 2layer neural network can be challenging. In the recent years many different approaches were proposed to learn 2layer neural networks under ... 
Partial Function Extension with Applications to Learning and Property Testing
(20191018)In partial function extension, we are given a partial function consisting of points from a domain and a function value at each point. Our objective is to determine if this partial function can be extended to a total ... 
Linear Size Sparsifier and the Geometry of the Operator Norm Ball
(20191007)The Matrix Spencer Conjecture asks whether given n symmetric matrices in ℝn×n with eigenvalues in [−1,1] one can always find signs so that their signed sum has singular values bounded by O(n‾√). The standard approach in ... 
Lagrangian Duality in Mechanism Design
(20190930)This talk surveys the usage of Lagrangian Duality in the design and analysis of auctions. Designing optimal (revenue maximizing) auctions in multiparameter settings has been among the most active areas in algorithmic ... 
Thompson Sampling for learning in online decision making
(20190923)Modern online marketplaces feed themselves. They rely on historical data to optimize content and userinteractions, but further, the data generated from these interactions is fed back into the system and used to optimize ... 
Approximating Profile Maximum Likelihood Efficiently
(20190909)Symmetric properties of distributions arise in multiple settings. For each of these, separate estimators and analysis techniques have been developed. Recently, Orlitsky et al showed that a single estimator that maximizes ... 
The Power of Factorization Mechanisms in Differential Privacy
(20190819)A central goal in private data analysis is to estimate statistics about an unknown distribution from a dataset possibly containing sensitive information, so that the privacy of any individual represented in the dataset is ... 
Solving Linear Programs in the Current Matrix Multiplication Time
(20190520)We show how to solve linear programs with accuracy epsilon in time n^{omega+o(1)} log(1/epsilon) where omega~2.3728639 is the current matrix multiplication constant. This hits a natural barrier of solving linear programs ... 
Abstract polymer models, the cluster expansion, and applications
(20190506)Will introduces abstract polymer models and the cluster expansion from statistical physics. He describes some of the original applications of these tools in statistical physics to understand phase transitions in lattice ... 
A constantfactor approximation algorithm for asymmetric TSP [Lecture 3]
(20190425)The traveling salesman problem is one of the most fundamental optimization problems. Given cities and pairwise distances, it is the problem of finding a tour of minimum distance that visits each city once. In spite of ... 
Different approaches for asymmetric TSP [Lecture 2]
(20190424)The traveling salesman problem is one of the most fundamental optimization problems. Given cities and pairwise distances, it is the problem of finding a tour of minimum distance that visits each city once. In spite of ...