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Mikel Landajuela
Person information
- affiliation: Lawrence Livermore National Laboratory, San Francisco Bay Area, CA, USA
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2020 – today
- 2024
- [i6]Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel M. Faissol, Brenden K. Petersen, Mikel Landajuela:
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces. CoRR abs/2412.11051 (2024) - 2023
- [i5]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer
, Guilherme Espada, Leon Ingelse, Alcides Fonseca
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - 2022
- [c5]Mikel Landajuela
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
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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