Foundations of AI Experts
352-294-6670
sitharam@cise.ufl.edu
Theory/foundations (e.g. sample size bounds for maximum likelihood estimation, dictionary learning, and boolean function learning); complexity theory; geometric modeling and constraint solving; algorithms and discrete modeling.
Projects
Event Memories using the Value of Information
José PríncipeQuantum Information Potential Fields: A Novel Uncertainty Quantification Framework for Machine Learning
José PríncipeImproving Reinforcement Learning Efficacy with Causality, Trajectories, and the Value of Information
José PríncipeA Biologically Inspired Event Memory Architecture for Machine Learning
José PríncipeOR-DRD-AI2020: What mind matters? Machine learning approaches to linking structural variation in the brain to individual differences in spatial behavior
S WeisbergReinforcement Learning and Kullback-Leibler Stochastic Optimal Control for Complex Networks
Sean MeynRegaining in Reinforcement Learning
Benjamin Van Roy (Stanford)An Active Architecture for Self-Learning
José PríncipeAdaptive Manifold Learning for Multi-Sensor Translation and Fusion given Missing Data
Alina ZarePhase I IUCRC University of Florida: Center for Big Learning
José PríncipeHeterogeneous Computing for Acceleration of Deep Learning
Herman LamCAREER: Supervised Learning for Incomplete and Uncertain Data
Alina Zare