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.

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Recent Submissions

  • Approximation Schemes for Planar Graphs: A Survey of Methods 

    Klein, Philip (2017-03-27)
    In addressing an NP-hard 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 

    Vempala, Santosh (Georgia Institute of Technology, 2017-03-15)
    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 

    Rozell, Christopher (Georgia Institute of Technology, 2017-03-15)
    The natural stimuli that biological vision must use to understand the world are extremely complex. Recent advances in machine learning have shown that low-dimensional geometric models (e.g., sparsity, manifolds) can ...
  • Neural Computations for Active Perception 

    Olshausen, Bruno (Georgia Institute of Technology, 2017-03-15)
    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 

    Daskalakis, Constantinos (2017-03-06)
    The Expectation-Maximization (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 

    Paturi, Ramamohan (2017-02-13)
    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 

    Kyng, Rasmus (2016-11-28)
    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 Two-Source Extractors and Resilient Functions 

    Zuckerman, David (2016-11-14)
    We explicitly construct an extractor for two independent sources on n bits, each with min-entropy 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 

    Raskhodnikova, Sofya (2016-11-07)
    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 

    Moitra, Ankur (2016-10-31)
    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 

    Kleinberg, Jon (2016-10-24)
    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 machine-learning prediction ...
  • From Minimum Cut to Submodular Minimization: Leveraging the Decomposable Structure 

    Ene, Alina (2016-10-17)
    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 

    Lucier, Brendan (2016-10-03)
    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 

    Karger, David (2016-09-26)
    The following procedure yields an unbiased estimator for the disconnection probability of an n-vertex 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 

    Gharan, Shayan Oveis (2016-09-12)
    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 0-1 random variables whose generating polynomial is stable. ...
  • Algorithmic Pricing 

    Blum, Avrim (Georgia Institute of Technology, 2013-04-08)
    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 

    Alon, Noga (Georgia Institute of Technology, 2012-08-28)
    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 

    Blekherman, Greg (Georgia Institute of Technology, 2012-08-28)
  • Robotics and Vision 

    Dellaert, Frank (Georgia Institute of Technology, 2012-08-28)

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