Algorithms, Randomness and Complexity Center (ARC)
Our mission
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 thinktank 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

ARC5 Distinguished Lecture [5]
Klaus Advanced Computing Building  August 28, 2012 
ARC Talks and Events [16]
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 
Modern Aspects of Submodularity [27]
March 1922, 2012 at Georgia Tech
Recent Submissions

Approximation Schemes for Planar Graphs: A Survey of Methods
(20170327)In addressing an NPhard problem in combinatorial optimization, one way to cope is to use an approximation scheme, an algorithm that, for any given ϵ>0, produces a solution whose value is within a 1+ϵ factor of optimal. ... 
A Computer Science View of the Brain
(Georgia Institute of Technology, 20170315)Computational perspectives on scientific phenomena have often proven to be remarkably insightful. Rapid advances in computational neuroscience, and the resulting plethora of data and models highlight the lack of an ... 
Optimal Sensory Coding Theories for Neural Systems Under Biophysical Constraints
(Georgia Institute of Technology, 20170315)The natural stimuli that biological vision must use to understand the world are extremely complex. Recent advances in machine learning have shown that lowdimensional geometric models (e.g., sparsity, manifolds) can ... 
Neural Computations for Active Perception
(Georgia Institute of Technology, 20170315)The human visual system does not passively view the world, but actively moves its sensor array through eye, head and body movements. How do neural circuits in the brain control and exploit these movements in order to build ... 
Ten Steps of EM Suffice for Mixtures of Two Gaussians
(20170306)The ExpectationMaximization (EM) algorithm is a widely used method for maximum likelihood estimation in models with latent variables. For estimating mixtures of Gaussians, its iteration can be viewed as a soft version of ... 
Genesis of ETH and SETH
(20170213)Several researchers have found ETH (Exponential Time Hypothesis) and Strong ETH to be useful and proved matching lower bounds for several problems in P as well as NP based on these conjectures. In this talk, I will talk ... 
Approximate Gaussian Elimination for Laplacians: Fast, Sparse, and Simple
(20161128)We show how to perform sparse approximate Gaussian elimination for Laplacian matrices. We present a simple, nearly linear time algorithm that approximates a Laplacian by a matrix with a sparse Cholesky factorization – the ... 
Explicit TwoSource Extractors and Resilient Functions
(20161114)We explicitly construct an extractor for two independent sources on n bits, each with minentropy at least log^C n for a large enough constant C. Our extractor outputs one bit and has error n^{\Omega(1)}. The best previous ... 
Differentially Private Analysis of Graphs
(20161107)Many types of data can be represented as graphs, where nodes correspond to individuals and edges capture relationships between them. Examples include datasets capturing “friendships” in an online social network, financial ... 
Robust Statistics, Revisited
(20161031)Starting from the seminal works of Tukey (1960) and Huber (1962), the field of robust statistics asks: Are there estimators that provable work in the presence of noise? The trouble is that all known provably robust estimators ... 
Human Decisions and Machine Predictions
(20161024)An increasing number of domains are providing us with detailed trace data on human decisions, often made by experts with deep experience in the subject matter. This provides an opportunity to use machinelearning prediction ... 
From Minimum Cut to Submodular Minimization: Leveraging the Decomposable Structure
(20161017)Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose ... 
Prices, Auctions, and Combinatorial Prophet Inequalities
(20161003)The most common way to sell resources, from apples to business licenses to concert tickets, is to post prices. A choice of prices can be viewed as an algorithm for an online stochastic optimization problem, which makes ... 
A Fast and Simple Unbiased Estimator for Network (Un)reliability
(20160926)The following procedure yields an unbiased estimator for the disconnection probability of an nvertex graph with minimum cut c if every edge fails independently with probability p: (i) contract every edge independently ... 
Strongly Rayleigh distributions and their Applications in Algorithm Design
(20160912)A multivariate polynomial p(z1,...,zn) is stable if p(z1,...,zn) <> 0 whenever Im(zi)>0 for all i. Strongly Rayleigh distributions are probability distributions on 01 random variables whose generating polynomial is stable. ... 
Algorithmic Pricing
(Georgia Institute of Technology, 20130408)Pricing and allocating goods to buyers with complex preferences in order to maximize some desired objective (e.g., social welfare or profit) is a central problem in Algorithmic Mechanism Design. In this talk I will discuss ... 
On Graphs, Arithmetic Progressions and Communication
(Georgia Institute of Technology, 20120828)Tools from extremal graph theory are helpful in the study of problems in additive number theory, theoretical computer science, and information theory. I will illustrate this fact by several closely related examples focusing ... 
Sums of Squares In Optimization
(Georgia Institute of Technology, 20120828) 
Robotics and Vision
(Georgia Institute of Technology, 20120828)