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    On the Fundamental Tradeoffs between Routing Table Size and Network Diameter in Peer-to-Peer Networks 

    Xu, Jun (Georgia Institute of Technology, 2002)
    In this work, we study a fundamental tradeoff issue in designing dynamic hash table (DHT) in peer-to-peer networks: the size of the routing table v.s. the network diameter. It was observed in Ratnasamy et al. that existing ...
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    Expectation-Oriented Framework for Automating Approximate Programming 

    Esmaeilzadeh, Hadi; Ni, Kangqi; Naik, Mayur (Georgia Institute of Technology, 2013)
    This paper describes ExpAX, a framework for automating approximate programming based on programmer-specified error expectations. Three components constitute ExpAX: (1) a programming model based on a new kind of program ...
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    Robust approaches and optimization for 3D data 

    Sawhney, Rahul (Georgia Institute of Technology, 2018-04-06)
    We introduce a robust, purely geometric, representation framework for fundamental association and analysis problems involving multiple views and scenes. The framework utilizes surface patches / segments as the underlying ...
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    Power Optimization of Embedded Memory Systems via Data Remapping 

    Palem, Krishna V.; Rabbah, Rodric Michel; Mooney, Vincent; Korkmaz, Pinar; Puttaswamy, Kiran (Georgia Institute of Technology, 2002)
    In this paper, we provide a novel compile-time data remapping algorithm that runs in linear time. This remapping algorithm is the first fully automatic approach applicable to pointer-intensive dynamic applications. We ...
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    Convergence in min-max optimization 

    Lai, Kevin A. (Georgia Institute of Technology, 2020-04-20)
    Min-max optimization is a classic problem with applications in constrained optimization, robust optimization, and game theory. This dissertation covers new convergence rate results in min-max optimization. We show that the ...
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    Optimization-driven emergence of deep hierarchies with applications in data mining and evolution 

    Siyari, Payam (Georgia Institute of Technology, 2018-11-09)
    It is well known that many complex systems, in both nature and technology, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex ...
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    Robot Calligraphy using Pseudospectral Optimal Control and a Simulated Brush Model 

    Chen, Jiaqi (Georgia Institute of Technology, 2019-12)
    Chinese calligraphy is unique and has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to ...
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    What 2-layer neural nets can we optimize? 

    Ge, Rong (2019-10-28)
    Optimizing neural networks is a highly nonconvex problem, and even optimizing a 2-layer neural network can be challenging. In the recent years many different approaches were proposed to learn 2-layer neural networks under ...
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    Group Fairness in Combinatorial Optimization 

    Munagala, Kamesh (2021-11-08)
    Consider the following classical network design model. There are n clients in a multi-graph with a single sink node. Each edge has a cost to buy, and a length if bought; typically, costlier edges have smaller lengths. There ...
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    Variations of Submodularity and Diversity: from Robust Optimization to Markov Chains 

    Jegelka, Stefanie (2017-09-25)
    The combinatorial concept of submodular set functions has proved to be a very useful discrete structure for optimization in machine learning and its applications. In this talk, I will show recent work on generalizations ...
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