Daniel M. Faissol

According to our database, Daniel M. Faissol authored at least 11 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement.

Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden K. Petersen, Daniel M. Faissol, Robert W. Anderson

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Reinforcement Learning for Adaptive Mesh Refinement.

Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

CoRR, 2021

Improving exploration in policy gradient search: Application to symbolic optimization.

Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

CoRR, 2021

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering symbolic policies with deep reinforcement learning.

Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Single Episode Policy Transfer in Reinforcement Learning.

Jiachen Yang, Brenden K. Petersen, Hongyuan Zha, Daniel M. Faissol

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

J. Comput. Biol., 2019

2018

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

CoRR, 2018

2016

Physics and optimal routing for urban radiation source search.

Richard Wheeler, Daniel M. Faissol, Claudio Santiago, Thomas Baginski, Karl Nelson

Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2016

2013

Exploitation of Ambiguous Cues to Infer Terrorist Activity.

Kevin S. Ni, Daniel M. Faissol, Thomas Edmunds, Richard Wheeler

Decis. Anal., 2013