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ORCIDMikel Landajuela
Person information
- affiliation: Lawrence Livermore National Laboratory, San Francisco Bay Area, CA, USA
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2020 – today
- 2023
[i5]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca
, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023)- 2022
- [c5]Mikel Landajuela, Rushil Anirudh, Joe Loscazo, Robert Blake:
Intracardiac Electrical Imaging Using the 12-Lead ECG: A Machine Learning Approach Using Synthetic Data. CinC 2022: 1-4 - [c4]Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen:
A Unified Framework for Deep Symbolic Regression. NeurIPS 2022 - 2021
- [c3]Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk, Cláudio Prata Santiago, Sookyung Kim, Joanne Taery Kim:
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. ICLR 2021 - [c2]Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Discovering symbolic policies with deep reinforcement learning. ICML 2021: 5979-5989 - [c1]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS 2021: 24912-24923 - [i4]Joanne Taery Kim, Mikel Landajuela, Brenden K. Petersen:
Distilling Wikipedia mathematical knowledge into neural network models. CoRR abs/2104.05930 (2021) - [i3]Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Improving exploration in policy gradient search: Application to symbolic optimization. CoRR abs/2107.09158 (2021) - [i2]Brenden K. Petersen, Cláudio P. Santiago, Mikel Landajuela:
Incorporating domain knowledge into neural-guided search. CoRR abs/2107.09182 (2021) - [i1]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR abs/2111.00053 (2021)
2010 – 2019
- 2015
- [j1]Miguel A. Fernández, Mikel Landajuela, Marina Vidrascu:
Fully decoupled time-marching schemes for incompressible fluid/thin-walled structure interaction. J. Comput. Phys. 297: 156-181 (2015)
Coauthor Index
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