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 ...

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 ...

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 ...

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 ...

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, ...

In this thesis, we deal with two problems on multidimensional allocation, specifically in apportionment and in bin packing. The apportionment problem models the allocation of seats in a House of Representatives such that ...

Data science has provided unprecedented opportunities to learn new insights and to predict, recommend, cluster, classify, transform, and optimize. Catalyzed by large-scale, networked computer systems, vast availability of ...

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 ...

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. ...

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 ...