A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties

no code implementations • 22 Feb 2024 • David J. Schodt, Ryan Brown, Michael Merritt, Samuel Park, Delsin Menolascino, Mark A. Peot

Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications.

Variational Inference

Fair Influence Maximization: A Welfare Optimization Approach

no code implementations • 14 Jun 2020 • Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice, Milind Tambe

Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter.

Fairness Management

Uncertainty-based Modulation for Lifelong Learning

no code implementations • 27 Jan 2020 • Andrew Brna, Ryan Brown, Patrick Connolly, Stephen Simons, Renee Shimizu, Mario Aguilar-Simon

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments.

One-Shot Learning