Cláudio P. Santiago

According to our database, Cláudio P. Santiago authored at least 13 papers between 2009 and 2022.

Collaborative distances:

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

2022

A Unified Framework for Deep Symbolic Regression.

Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Incorporating domain knowledge into neural-guided search.

Brenden K. Petersen, Cláudio P. Santiago, Mikel Landajuela

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

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim

Proceedings of the 9th International Conference on Learning Representations, 2021

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

An efficient algorithm for the projection of a point on the intersection of two hyperplanes and a box in \(\mathbb {R}^n\).

Cláudio P. Santiago, Sérgio Assunção Monteiro, Helder Inácio, Nelson Maculan, Maria Helena Jardim

EURO J. Comput. Optim., 2019

2018

A new algorithm for the small-field astrometric point-pattern matching problem.

Cláudio P. Santiago, Carlile Lavor, Sérgio Assunção Monteiro, Alberto Krone-Martins

J. Glob. Optim., 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

2009

On the nonnegative least squares.

Cláudio P. Santiago

PhD thesis, 2009