***Bryan:
Both cognitive constraints and limited amounts of data restrict the complexity of inferential models. Sparse coding is an elegant way to address these restrictions, extracting correlated subparts from data in a way that can be efficient, predictive, and adaptive. We present a sparse coding method for use on binary data and use it to study correlated groups in conflict in a macaque society. The method reveals specific groups as predictable components of conflict in our data, and a description of collective behavior in terms of sparse groups can be shown to be cognitively efficient.
Both cognitive constraints and limited amounts of data restrict the complexity of inferential models. Sparse coding is an elegant way to address these restrictions, extracting correlated subparts from data in a way that can be efficient, predictive, and adaptive. We present a sparse coding method for use on binary data and use it to study correlated groups in conflict in a macaque society. The method reveals specific groups as predictable components of conflict in our data, and a description of collective behavior in terms of sparse groups can be shown to be cognitively efficient.
***Hyemin:
A mixed binary set constrained by convex, nonseparable quadratic functions appears as a substructure in many practical mixed integer nonlinear programs (MINLPs) including portfolio management or model selection. We aim to obtain a good approximation of its convex hull, and our approach starts by transforming the set using Cholesky factorization. A number of valid nonlinear inequalities for the transformed set are derived, most of which are represented as second-order cone constraints. Computational experiments were conducted to compare the relaxations to the convex hull.
February 15 @ 12:30
12:30 pm (1h)
Discovery Building, Orchard View Room
Bryan Daniels, Hyemin Jeon