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Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data.
Algebraic simplification
Buchberger, B. and Loos, R · 1982
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Temporal credit assignment in reinforcement learning
Sutton, R. S · 1984
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Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
Schmidhuber, J · 1987
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Neural networks: a comprehensive foundation
Haykin, S · 1994
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Genetic programming as a means for programming computers by natural selection
Koza, J. R · 1994
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Probabilistic incremental program evolution
Salustowicz, R. and Schmidhuber, J · 1997
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Linkage learning via probabilistic modeling in the ECGA
Harik, G. et al · 1999
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On the behavioral diversity of random programs
Looks, M · 2007
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A field guide to genetic programming, 2008
Poli, R., Langdon, W. B., and McPhee, N. F · 2008
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Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming
Vladislavleva, E. J., Smits, G. F., and Den Hertog, D · 2008
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Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
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A survey of monte carlo tree search methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S · 2012
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An investigation of local patterns for estimation of distribution genetic programming
Hemberg, E., Veeramachaneni, K., McDermott, J., Berzan, C., and O’Reilly, U.-M · 2012
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Learning to learn
Thrun, S. and Pratt, L · 2012
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Probabilistic model building in genetic programming: A critical review
Kim, K., Shan, Y., Nguyen, X. H., and McKay, R. I · 2014
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Complexity measures for multi-objective symbolic regression
Kommenda, M., Beham, A., Affenzeller, M., and Kronberger, G · 2015
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The programming game: evaluating mcts as an alternative to gp for symbolic regression
White, D. R., Yoo, S., and Singer, J · 2015
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Semantic geometric initialization
Pawlak, T. P. and Krawiec, K · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Thinking fast and slow with deep learning and tree search
Anthony, T., Tian, Z., and Barber, D · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Sympy: symbolic computing in python
Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., et al · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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A comparison of semantic-based initialization methods for genetic programming
Parameter identification for symbolic regression using nonlinear least squares
Kommenda, M., Burlacu, B., Kronberger, G., and Affenzeller, M · 2020
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Generative language modeling for automated theorem proving
Polu, S. and Sutskever, I · 2020
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Ai feynman: A physics-inspired method for symbolic regression
Udrescu, S.-M. and Tegmark, M · 2020
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Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
Vaddireddy, H., Rasheed, A., Staples, A. E., and San, O · 2020
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Learning a formula of interpretability to learn interpretable formulas
Virgolin, M., De Lorenzo, A., Medvet, E., and Randone, F · 2020
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Ahmad, H. and Helmuth, T · 2018
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Structural risk minimization-driven genetic programming for enhancing generalization in symbolic regression
Chen, Q., Zhang, M., and Xue, B · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
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Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
Hernandez, A., Balasubramanian, A., Yuan, F., Mason, S. A., and Mueller, T · 2019
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Deep learning for symbolic mathematics
Lample, G. and Charton, F · 2019
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Neural-guided symbolic regression with asymptotic constraints
Li, L., Fan, M., Singh, R., and Riley, P · 2019
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Petersen, B. K., Larma, M. L., Mundhenk, T. N., Santiago, C. P., Kim, S. K., and Kim, J. T · 2019
Cited alongside, same era.
Neural symbolic regression that scales, 2021
Biggio, L., Bendinelli, T., Neitz, A., Lucchi, A., and Parascandolo, G · 2021
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Application of symbolic regression for constitutive modeling of plastic deformation
Kabliman, E., Kolody, A. H., Kronsteiner, J., Kommenda, M., and Kronberger, G · 2021
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Contemporary symbolic regression methods and their relative performance
La Cava, W., Orzechowski, P., Burlacu, B., de Franca, F. O., Virgolin, M., Jin, Y., Kommenda, M., and Moore, J. H · 2021
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Incorporating actor-critic in monte carlo tree search for symbolic regression
Lu, Q., Tao, F., Zhou, S., and Wang, Z · 2021
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Symbolic regression via neural-guided genetic programming population seeding
Mundhenk, T. N., Landajuela, M., Glatt, R., Santiago, C. P., Faissol, D. M., and Petersen, B. K · 2021
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Symbolicgpt: A generative transformer model for symbolic regression
Valipour, M., You, B., Panju, M., and Ghodsi, A · 2021
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Improving model-based genetic programming for symbolic regression of small expressions
Virgolin, M., Alderliesten, T., Witteveen, C., and Bosman, P. A · 2021
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Discovering faster matrix multiplication algorithms with reinforcement learning
Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R Ruiz, F. J., Schrittwieser, J., Swirszcz, G., et al · 2022
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End-to-end symbolic regression with transformers
Kamienny, P.-A., d’Ascoli, S., Lample, G., and Charton, F · 2022
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Hypertree proof search for neural theorem proving
Lample, G., Lachaux, M.-A., Lavril, T., Martinet, X., Hayat, A., Ebner, G., Rodriguez, A., and Lacroix, T · 2022
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A unified framework for deep symbolic regression
Landajuela, M., Lee, C., Yang, J., Glatt, R., Santiago, C. P., Aravena, I., Mundhenk, T. N., Mulcahy, G., and Petersen, B. K · 2022
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Evolving symbolic density functionals
Ma, H., Narayanaswamy, A., Riley, P., and Li, L · 2022
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Symbolic physics learner: Discovering governing equations via monte carlo tree search
Sun, F., Liu, Y., Wang, J.-X., and Sun, H · 2022
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Symbolic regression is NP-hard
Virgolin, M. and Pissis, S. P · 2022
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