Genetic Programming Bibliography entries for Chak Shing Lee

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GP coauthors/coeditors: Fabricio Olivetti de Franca, Marco Virgolin, Michael Kommenda, Maimuna Majumder, Miles Cranmer, Guilherme Jorge Nunes Monteiro Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden Kyle Petersen, Ruben Glatt, T Nathan Mundhenk, Jacob Dean Hochhalter, David L Randall, Pierre-Alexandre Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Machado, Casper Wilstrup, William La Cava, Jiachen Yang, Claudio P Santiago, Ignacio Andres Aravena Solis, Garrett Mulcahy,

Genetic Programming Articles by Chak Shing Lee

• F. O. de Franca and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. La Cava. SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Transactions on Evolutionary Computation. Early Access. details

Genetic Programming conference papers by Chak Shing Lee

• Mikel Landajuela and Chak Shing Lee and Jiachen Yang and Ruben Glatt and Claudio P. Santiago and Ignacio Aravena and Terrell Nathan Mundhenk and Garrett Mulcahy and Brenden K. Petersen. A Unified Framework for Deep Symbolic Regression. In Sanmi Koyejo and S. Mohamed and A. Agarwal and Danielle Belgrave and K. Cho and A. Oh editors, Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems, NeurIPS 2022, pages 33985-33998, New Orleans, USA, 2022. Curran Associates, Inc.. details

Genetic Programming other entries for Chak Shing Lee

• F. O. de Franca and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. La Cava. Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. 2023. v3. details