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Bayesian Optimization is ubiquitous in experimental design and black-box optimization for improving search efficiency.
A fast and elitist multiobjective genetic algorithm: Nsga-ii
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan · 2002
Earlier work this paper cites.
Efficient benchmarking of hyperparameter optimizers via surrogates
K. Eggensperger, F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
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Google vizier: A service for black-box optimization
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
T. Kudo and J. Richardson · 2018
Earlier work this paper cites.
Tune: A research platform for distributed model selection and training
R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. E. Gonzalez, and I. Stoica · 2018
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Optuna: A next-generation hyperparameter optimization framework
T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama · 2019
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COCO: the large scale black-box optimization benchmarking (bbob-largescale) test suite
O. A. ElHara, K. Varelas, D. M. Nguyen, T. Tusar, D. Brockhoff, N. Hansen, and A. Auger · 2019
Earlier work this paper cites.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Pyglove: Symbolic programming for automated machine learning
D. Peng, X. Dong, E. Real, M. Tan, Y. Lu, G. Bender, H. Liu, A. Kraft, C. Liang, and Q. Le · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
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Automl-zero: Evolving machine learning algorithms from scratch
E. Real, C. Liang, D. R. So, and Q. V. Le · 2020
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Y. Chung, I. Char, H. Guo, J. Schneider, and W. Neiswanger · 2021
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The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Earlier work this paper cites.
Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
B. X. Ru, X. Wan, X. Dong, and M. A. Osborne · 2021
Earlier work this paper cites.
BANANAS: bayesian optimization with neural architectures for neural architecture search
C. White, W. Neiswanger, and Y. Savani · 2021
Earlier work this paper cites.
Towards learning universal hyperparameter optimizers with transformers
Y. Chen, X. Song, C. Lee, Z. Wang, R. Zhang, D. Dohan, K. Kawakami, G. Kochanski, A. Doucet, M. Ranzato, S. Perel, and N. de Freitas · 2022
Cited alongside, same era.
Bayesian optimization over permutation spaces
A. Deshwal, S. Belakaria, J. R. Doppa, and D. H. Kim · 2022
Cited alongside, same era.
What can transformers learn in-context? A case study of simple function classes
S. Garg, D. Tsipras, P. Liang, and G. Valiant · 2022
Cited alongside, same era.
Why do tree-based models still outperform deep learning on typical tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux · 2022
Cited alongside, same era.
Local latent space bayesian optimization over structured inputs
N. Maus, H. Jones, J. Moore, M. J. Kusner, J. Bradshaw, and J. R. Gardner · 2022
Cited alongside, same era.
Transformers can do bayesian inference
S. Müller, N. Hollmann, S. Pineda-Arango, J. Grabocka, and F. Hutter · 2022
Bochemian: Large language model embeddings for bayesian optimization of chemical reactions
B. Ranković and P. Schwaller · 2023
Later among the works it cites.
Mathematical discoveries from program search with large language models
B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Kumar, E. Dupont, F. J. Ruiz, J. S. Ellenberg, P. Wang, O. Fawzi, et al · 2023
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Tree of thoughts: Deliberate problem solving with large language models
S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, and K. Narasimhan · 2023
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Transfer learning for bayesian optimization on heterogeneous search spaces
Z. Fan, X. Han, and Z. Wang · 2024
Closest in time.
Promptbreeder: Self-referential self-improvement via prompt evolution
C. Fernando, D. Banarse, H. Michalewski, S. Osindero, and T. Rocktäschel · 2024
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Gemini: A family of highly capable multimodal models, 2024
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Cited alongside, same era.
Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
T. Nguyen and A. Grover · 2022
Cited alongside, same era.
Batch bayesian optimization on permutations using the acquisition weighted kernel
C. Oh, R. Bondesan, E. Gavves, and M. Welling · 2022
Cited alongside, same era.
Introducing chatgpt
OpenAI · 2022
Cited alongside, same era.
Efficient transformers: A survey
Y. Tay, M. Dehghani, D. Bahri, and D. Metzler · 2022
Cited alongside, same era.
Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks
A. Zela, J. N. Siems, L. Zimmer, J. Lukasik, M. Keuper, and F. Hutter · 2022
Cited alongside, same era.
Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Y. Bai, F. Chen, H. Wang, C. Xiong, and S. Mei · 2023
Cited alongside, same era.
Google · 2024
Closest in time.
Localized zeroth-order prompt optimization
W. Hu, Y. Shu, Z. Yu, Z. Wu, X. Lin, Z. Dai, S. Ng, and B. K. H. Low · 2024
Closest in time.
A sober look at llms for material discovery: Are they actually good for bayesian optimization over molecules?
A. Kristiadi, F. Strieth-Kalthoff, M. Skreta, P. Poupart, A. Aspuru-Guzik, and G. Pleiss · 2024
Closest in time.
Let’s verify step by step
H. Lightman, V. Kosaraju, Y. Burda, H. Edwards, B. Baker, T. Lee, J. Leike, J. Schulman, I. Sutskever, and K. Cobbe · 2024
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Large language models to enhance bayesian optimization
T. Liu, N. Astorga, N. Seedat, and M. van der Schaar · 2024
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LICO: large language models for in-context molecular optimization
T. Nguyen and A. Grover · 2024
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R. Vacareanu, V. Negru, V. Suciu, and M. Surdeanu · 2024
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Pre-trained gaussian processes for bayesian optimization
Z. Wang, G. E. Dahl, K. Swersky, C. Lee, Z. Nado, J. Gilmer, J. Snoek, and Z. Ghahramani · 2024
Closest in time.
Trained transformers learn linear models in-context
R. Zhang, S. Frei, and P. L. Bartlett · 2024
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Performance prediction for large systems via text-to-text regression, 2025
Y. Akhauri, B. Lewandowski, C.-H. Lin, A. N. Reyes, G. C. Forbes, A. Wongpanich, B. Yang, M. S. Abdelfattah, S. Perel, and X. Song · 2025
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Understanding LLM embeddings for regression
E. Tang, B. Yang, and X. Song · 2025
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