# Machine Learning

## Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications

## Learning Signal Temporal Logic through Neural Network for Interpretable Classification

## Time-Incremental Learning of Temporal Logic Classifiers Using Decision Trees

## Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications

## Learning A Risk-Aware Trajectory Planner From Demonstrations Using Logic Monitor

## Differentiable Logic Layer for Rule Guided Trajectory Prediction

## Learning An Explainable Trajectory Generator Using The Automaton Generative Network (AGN)

## Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

## Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior

Brandon Araki, Kiran Vodrahalli, Thomas Leech, **Cristian Ioan Vasile**, Mark Donahue, and Daniela Rus. *Deep Bayesian Non-parametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior*. In AAAI Conference on Artificial Intellifence, New York, NY, USA, July 2020.