activity
20152022
most citedEvolving Shepherding Behavior with Genetic Programming Algorithms

6 citations · 19 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LO2022★ 1 cited

Envelopes and Waves: Safe Multivehicle Collision Avoidance for Horizontal Non-deterministic Turns

Yanni Kouskoulas, T. J. Machado, Daniel Genin +3

We present an approach to analyzing the safety of asynchronous, independent, non-deterministic, turn-to-bearing horizontal maneuvers for two vehicles. Future turn rates, final bear…

cs.LG2020★ 2 cited

Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples

Ivan Papusha, Rosa Wu, Joshua Brulé +3

There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input…

cs.PL2018

Whittemore: An embedded domain specific language for causal programming

Joshua Brulé

This paper introduces Whittemore, a language for causal programming. Causal programming is based on the theory of structural causal models and consists of two primary operations: i…

stat.ME2018

Causal programming: inference with structural causal models as finding instances of a relation

Joshua Brulé

This paper proposes a causal inference relation and causal programming as general frameworks for causal inference with structural causal models. A tuple, $\langle M, I, Q, F \rangl…

stat.ME2017★ 2 cited

A causation coefficient and taxonomy of correlation/causation relationships

Joshua Brulé

This paper introduces a causation coefficient which is defined in terms of probabilistic causal models. This coefficient is suggested as the natural causal analogue of the Pearson…

cs.AI2016★ 6 cited

Evolving Shepherding Behavior with Genetic Programming Algorithms

Joshua Brulé, Kevin Engel, Nick Fung +1

We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of ani…

cs.AI2016★ 2 cited

The Computational Power of Dynamic Bayesian Networks

Joshua Brulé

This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models…

cs.HC2015★ 6 cited

MetroViz: Visual Analysis of Public Transportation Data

Fan Du, Joshua Brulé, Peter Enns +2

Understanding the quality and usage of public transportation resources is important for schedule optimization and resource allocation. Ridership and adherence are the two main dime…