Machine Learning holds large potential for changing how the Air Force operates from advanced reasoning over massive heterogeneous data to learning adaptive models for multi-agent planning, strategy development, and decision making. However, key challenges must be first addressed before this potential is fully realized. This talk will first highlight key …
We study the representability of sets that admit extended formulations using mixed-integer bilevel programs. We show that feasible regions modeled by continuous bilevel constraints (with no integer variables), complementarity constraints, and polyhedral reverse convex constraints are all finite unions of polyhedra. Conversely, any finite union of polyhedra can be represented …
The rapid advancements of internet of things (IoT) technology and cyber-physical infrastructure have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for performance improvement in various complex systems. Meanwhile, it also raises new research challenges on data analysis and decision making, such as heterogeneous data …
I will use linear regression as a guinea pig to illustrate data poisoning attacks in adversary machine learning. An adversary attempts to fool linear regression into learning some wrong regression coefficients: perhaps customers are more satisfied the longer they sit in your waiting room, or maybe Wisconsin isn’t warming. The …
Machine Learning and Data Science (ML) is starting to take the place in industry that “Information Technology” had in the late 1990s: businesses of all sizes and in all sectors, are recognizing how necessary it has become to develop predictive capabilities for continued profitability of their core competencies. To be …
Optimal mass transport (OMT) is a rich area of research with applications to numerous disciplines including econometrics, physics, signal processing, systems and controls, data science and so on. In this talk, I will give a brief introduction to OMT and then focus on its recent developments in algorithms and applications. …
Quadratic assignment is a very general problem in theoretical computer science. It includes graph matching, the traveling salesman problem, and the Gromov-Hausdorff distance between finite metric spaces as particular cases. Quadratic assignment is in general NP-hard and even hard to approximate, but in fact the problem can be tractable for …
Although half a decade has passed since Frank Rosenblatt’s original work on multi- layer perceptrons, modern artificial neural networks are still surprisingly similar to his original ideas. In this talk I will question one of their most fundamental design aspects. As networks have become much deeper than had been possible …
What do interstellar navigation and protein shape reconstruction have in common? They both require us to solve the quintessential distance geometry problem: find a set a points which realizes an observed set of distances. Though stars, spaceships and proteins all tend to move, however, the textbook version of the distance …
This paper investigates asymptotic behaviors of gradient descent algorithms (particularly stochastic gradient descent and accelerated gradient descent) in the context of stochastic optimization arose in statistics and machine learning where objective functions are estimated from available data. We show that these algorithms can be modeled by continuous-time ordinary or stochastic …