Fetching the paper…
Reading the bibliography…
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts.
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2002) · 2002
Earlier work this paper cites.
Neural additive models: Interpretable machine learning with neural nets
Agarwal, R., Frosst, N., Zhang, X., Caruana, R., and Hinton, G. E. (2020) · 2004
Earlier work this paper cites.
A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice
Lee, K. S. and Geem, Z. W. (2005) · 2005
Earlier work this paper cites.
Learning to prove from synthetic theorems
Aygün, E., Ahmed, Z., Anand, A., Firoiu, V., Glorot, X., Orseau, L., Precup, D., and Mourad, S. (2020) · 2006
Earlier work this paper cites.
Differential privacy: A survey of results
Dwork, C. (2008) · 2008
Earlier work this paper cites.
Firefly algorithm, stochastic test functions and design optimisation
Yang, X.-S. (2010) · 2010
Earlier work this paper cites.
A review of population-based meta-heuristic algorithms
Beheshti, Z. and Shamsuddin, S. M. H. (2013) · 2013
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
Bergstra, J., Yamins, D., and Cox, D. (2013) · 2013
Earlier work this paper cites.
Learning navigation behaviors end-to-end with autorl
Chiang, H.-T. L., Faust, A., Fiser, M., and Francis, A. (2019) · 2014
Earlier work this paper cites.
Rprotobuf: Efficient cross-language data serialization in R
Eddelbuettel, D., Stokely, M., and Ooms, J. (2014) · 2014
Earlier work this paper cites.
Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J. T., Blum, M., and Hutter, F. (2015) · 2015
Earlier work this paper cites.
Automatic component-wise design of multiobjective evolutionary algorithms
Bezerra, L. C. T., López-Ibáñez, M., and Stützle, T. (2016) · 2016
Earlier work this paper cites.
The irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., and Stützle, T. (2016) · 2016
Earlier work this paper cites.
Google vizier: A service for black-box optimization
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., and Sculley, D. (2017) · 2017
Earlier work this paper cites.
Bayesian optimization for a better dessert
Kochanski, G., Golovin, D., Karro, J., Solnik, B., Moitra, S., and Sculley, D. (2017) · 2017
Earlier work this paper cites.
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., and Webster, D. R. (2017) · 2017
Earlier work this paper cites.
Such, F. P., Madhavan, V., Conti, E., Lehman, J., Stanley, K. O., and Clune, J. (2017) · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V. (2017) · 2017
Earlier work this paper cites.
Searching for efficient multi-scale architectures for dense image prediction
Chen, L., Collins, M. D., Zhu, Y., Papandreou, G., Zoph, B., Schroff, F., Adam, H., and Shlens, J. (2018) · 2018
Cited alongside, same era.
Stochastic local search
Hoos, H. H. and Stützle, T. (2018) · 2018
Cited alongside, same era.
Towards practical differential privacy for SQL queries
Johnson, N. M., Near, J. P., and Song, D. (2018) · 2018
Cited alongside, same era.
Simple random search of static linear policies is competitive for reinforcement learning
Mania, H., Guy, A., and Recht, B. (2018) · 2018
Cited alongside, same era.
Hpbandster
ML4AAD (2018) · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. (2018) · 2018
Cited alongside, same era.
Pyglove: Symbolic programming for automated machine learning
Peng, D., Dong, X., Real, E., Tan, M., Lu, Y., Bender, G., Liu, H., Kraft, A., Liang, C., and Le, Q. (2020) · 2020
Later among the works it cites.
A survey on distributed machine learning
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., and Rellermeyer, J. S. (2020) · 2020
Later among the works it cites.
Learning a multi-domain curriculum for neural machine translation
Wang, W., Tian, Y., Ngiam, J., Yang, Y., Caswell, I., and Parekh, Z. (2020) · 2020
Later among the works it cites.
Apollo: Transferable architecture exploration
Yazdanbakhsh, A., Angermüller, C., Akin, B., Zhou, Y., Jones, A., Hashemi, M., Swersky, K., Chatterjee, S., Narayanaswami, R., and Laudon, J. (2020) · 2020
Later among the works it cites.
Bayesian optimization for materials design with mixed quantitative and qualitative variables
Zhang, Y., Apley, D. W., and Chen, W. (2020) · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Evolving rewards to automate reinforcement learning
Faust, A., Francis, A. G., and Mehta, D. (2019) · 2019
Cited alongside, same era.
Emulation of physical processes with emukit
Paleyes, A., Pullin, M., Mahsereci, M., Lawrence, N., and González, J. (2019) · 2019
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V. (2019) · 2019
Cited alongside, same era.
High-quality ms/ms spectrum prediction for data-dependent and data-independent acquisition data analysis
Tiwary, S., Levy, R., Gutenbrunner, P., Soto, F. S., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., and Cox, J. (2019) · 2019
Cited alongside, same era.
Nas-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., and Hutter, F. (2019) · 2019
Cited alongside, same era.
Botorch: A framework for efficient monte-carlo bayesian optimization
Balandat, M., Karrer, B., Jiang, D. R., Daulton, S., Letham, B., Wilson, A. G., and Bakshy, E. (2020) · 2020
Cited alongside, same era.
Agarwal, R., Machado, M. C., Castro, P. S., and Bellemare, M. G. (2021) · 2021
Later among the works it cites.
Reverb: A framework for experience replay
Cassirer, A., Barth-Maron, G., Brevdo, E., Ramos, S., Boyd, T., Sottiaux, T., and Kroiss, M. (2021) · 2021
Later among the works it cites.
Evolving reinforcement learning algorithms
Co-Reyes, J. D., Miao, Y., Peng, D., Real, E., Le, Q. V., Levine, S., Lee, H., and Faust, A. (2021) · 2021
Later among the works it cites.
Adaptive experimentation platform
Facebook (2021) · 2021
Later among the works it cites.
Automl: A survey of the state-of-the-art
He, X., Zhao, K., and Chu, X. (2021) · 2021
Later among the works it cites.
Openbox: A generalized black-box optimization service
Li, Y., Shen, Y., Zhang, W., Chen, Y., Jiang, H., Liu, M., Jiang, J., Gao, J., Wu, W., Yang, Z., Zhang, C., and Cui, B. (2021) · 2021
Later among the works it cites.
Dataset distillation with infinitely wide convolutional networks
Nguyen, T., Novak, R., Xiao, L., and Lee, J. (2021) · 2021
Later among the works it cites.
Bayesian reaction optimization as a tool for chemical synthesis
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., and Doyle, A. G. (2021) · 2021
Later among the works it cites.
Assessing irace for automated machine and deep learning in computer vision
Vieira, C. E. M. (2021) · 2021
Later among the works it cites.
Using deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D., Sanderson, T., Carter, B., Sculley, D., DePristo, M. A., and Colwell, L. J. (2022) · 2022
Closest in time.
Smac3: A versatile bayesian optimization package for hyperparameter optimization
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., and Hutter, F. (2022) · 2022
Closest in time.
Automated reinforcement learning (autorl): A survey and open problems
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., and Lindauer, M. (2022) · 2022
Closest in time.
A full-stack search technique for domain optimized deep learning accelerators
Zhang, D., Huda, S., Songhori, E. M., Prabhu, K., Le, Q. V., Goldie, A., and Mirhoseini, A. (2022) · 2022
Closest in time.