Project Category: Foundations of AI

Improving Reinforcement Learning Efficacy with Causality, Trajectories, and the Value of Information

Reinforcement learning (RL) is very slow and does not scale up well to high dimensional spaces. This proposal employs causality to go beyond correlation learning to reduce the amount of data necessary to train RL. Pointwise interactions with the environment will be substituted by trajectories to speed up training, while the value of information optimizes the exploration exploitation dilemma.

Event Memories using the Value of Information

This proposal seeks to improve the architecture of the conventional state model of control theory by extracting events from the flow of time and storing them in specialized independent memory banks. The theory of the value of information will be employed to select the events and reduce the memory capacity.

CAREER: Supervised Learning for Incomplete and Uncertain Data

This CAREER project will advance the state of the art in supervised machine learning to allow for incomplete, uncertain and unspecific label information. Supervised machine learning algorithms produce desired outputs for given input data by learning from example training data. The methods generally rely on completely and accurately labeled training data to drive the learning algorithm. However, many applications are plagued with labels that are incomplete, uncertain, and unspecific (lack precision). Current techniques do not adequately handle such data.