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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 ...
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 ...
A Factor Graph Approach To Constrained Optimization
Several problems in robotics can be solved using constrained optimization. For example, solutions in areas like control and planning frequently use it. Meanwhile, the Georgia Tech Smoothing and Mapping (GTSAM) toolbox ...
LP and SDP extended formulations: Lower bounds and approximation algorithms
(Georgia Institute of Technology, 2017-05-24)
In this thesis we study various aspects of linear and semidefinite
programs including their limitations in approximating various combinatorial
optimization problems as well as applications of these
paradigms in solving ...
Optimization-driven emergence of deep hierarchies with applications in data mining and evolution
(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 ...
Robot Calligraphy using Pseudospectral Optimal Control and a Simulated Brush Model
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 ...
Snow Coverage Prediction using Machine Learning Techniques
Snow coverage is often predicted through analysis of satellite images. Two of the most common satellites used for predictions are MODIS and Landsat. Unfortunately, snow coverage predictions are limited either by MODIS ...
Optimal stochastic and distributed algorithms for machine learning
(Georgia Institute of Technology, 2013-07-08)
Stochastic and data-distributed optimization algorithms have received lots of attention from the machine learning community due to the tremendous demand from the large-scale learning and the big-data related optimization. ...
Syntactic foundations for machine learning
(Georgia Institute of Technology, 2013-04-08)
Machine learning has risen in importance across science, engineering, and business in
recent years. Domain experts have begun to understand how their data analysis problems
can be solved in a principled and efficient ...
Context Aware Policy Selection
In of optimal control and reinforcement learning, the difference in the performance of a state-of-the-art policy and a mediocre one is minuscule in comparison to their difference in amortized computational cost. Further, ...