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Variations of Submodularity and Diversity: from Robust Optimization to Markov Chains
(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 ...
Expectation-Oriented Framework for Automating Approximate Programming
(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 ...
On the Fundamental Tradeoffs between Routing Table Size and Network Diameter in Peer-to-Peer Networks
(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 ...
Power Optimization of Embedded Memory Systems via Data Remapping
(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 ...
Online algorithms and the k-server conjecture
(Georgia Institute of Technology, 2011-11-11)
Traditionally, in the problems considered in optimization, one needs to produce the solution only after the whole input is made available. However, in many real-world scenarios the input is revealed gradually, and one needs ...
Stochastic Gradient Descent with Only One Projection
(Georgia Institute of Technology, 2012-09-28)
Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays ...
Learning Submodular Functions
(Georgia Institute of Technology, 2009)
This paper considers the problem of learning submodular functions. A problem instance consists
of a distribution on {0,1}[superscript n] and a real-valued function on {0,1}[superscript n] that is non-negative, monotone ...
Incremental Light Bundle Adjustment for Robotics Navigation
(Georgia Institute of Technology, 2013-11)
This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches
are capable of processing incoming visual observations, incorporating ...
Efficient and principled robot learning: Theory and algorithms
(Georgia Institute of Technology, 2020-01-07)
Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed, robots must reason about sequential ...
What 2-layer neural nets can we optimize?
(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 ...