Discovery Building – Page 32

A Conditional Gaussian Framework for Uncertainty Quantification, Data Assimilation and Prediction of Complex Nonlinear Turbulent Dynamical Systems

A conditional Gaussian framework for uncertainty quantification, data assimilation and prediction of complex nonlinear turbulent dynamical systems will be introduced in this talk. Despite the conditional Gaussianity, the dynamics remain highly nonlinear and are able to capture strongly non-Gaussian features such as intermittency and extreme events. The conditional Gaussian structure …

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality

Tensors of order 3 or greater, known as higher-order tensors, have recently attracted increased attention in many fields. Methods built on tensors provide powerful tools to capture complex structures in data that lower-order methods may fail to exploit. However, extending familiar matrix concepts to higher-order tensors is not straightforward, and …

Towards an autonomous network of biological sensors

Bio-sensors are becoming an integral part of our everyday life, implicitly and explicitly. The successful operation and availability of biological circuits and components for data processing makes bacteria strong candidates to use as computing machines. Currently bio-sensors, including bacterial sensors are processed independently off-line, leading to delays, manual errors and …

Tight regret bounds for a latent variable model of recommendation systems

Adversarial Networks with Structured Inputs and Outputs

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 …

Mixed-integer bilevel representability

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 …

Big Data Analytics for Real-time Complex System Monitoring and Prognostics

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 …

How to Poison Linear Regression

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 …

A Marketplace for Data: An Algorithmic Solution

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: Theory, Algorithm, and Applications

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. …