Extreme deconvolution: Inferring complete distribution functions from noisy, heterogeneous and incomplete observations

Extreme deconvolution: inferring complete distribution functions from noisy, heterogeneous and incomplete observations

We generalize the well-known mixtures of Gaussians approach to density estimation and the accompanying Expectation-Maximization technique for finding the maximum likelihood parameters of the mixture to the case where each data point carries an individual d-dimensional uncertainty covariance and has unique missing data properties. This algorithm reconstructs the error-deconvolved or “underlying”...

Alignment and Assembly: Inferring Networks from Noisy Observations

Over recent years, many large network datasets have become available, giving rise to novel and valuable applications of data mining and machine learning techniques. These datasets include social networks, the structure of the internet, and protein-interaction networks in computational biology, to name just a few. Graph mining exploits information hidden in these data to shed light on such probl...

Inferring Trading Strategies from Probability Distribution Functions Inferring Trading Strategies from Probability Distribution Functions

Learning STRIPS Operators from Noisy and Incomplete Observations

Agents learning to act autonomously in realworld domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-wo...

Learning Influence Functions from Incomplete Observations

We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missing observations. Proper PAC learnability under the Discrete-Time Linear Thres...