Fetching the paper…
Reading the bibliography…
Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance, whose progress has been boosted by related benchmarks.
Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
Storn, R. and Price, K · 1997
Earlier work this paper cites.
Fast subsampling performance estimates for classification algorithm selection
Petrak, J · 2000
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Édouard Duchesnay · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y · 2012
Earlier work this paper cites.
Openml: networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijin, B. B. and Torgo, L · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
Multi-task bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2013
Earlier work this paper cites.
Freeze-thaw bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2014
Earlier work this paper cites.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, T., Springenberg, J. T., and Hutter, F · 2015
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
Earlier work this paper cites.
Bischl, B., Casalicchio, G., Feurer, M., Hutter, F., Lang, M., Mantovani, R. G., van Rijn, J. N., and Vanschoren, J · 2017
Earlier work this paper cites.
Fast bayesian optimization of machine learning hyperparameters on large datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F · 2017
Earlier work this paper cites.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y · 2017
Cited alongside, same era.
Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
Cited alongside, same era.
Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Towards federated unsupervised representation learning
van Berlo, B., Saeed, A., and Ozcelebi, T · 2020
Later among the works it cites.
Dehb: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization
Awad, N., Mallik, N., and Hutter, F · 2021
Later among the works it cites.
HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO
Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F · 2021
Later among the works it cites.
Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
Khodak, M., Tu, R., Li, T., Li, L., Balcan, N., Smith, V., and Talwalkar, A · 2021
Later among the works it cites.
HPO-B: A large-scale reproducible benchmark for black-box HPO based on openml
Pineda-Arango, S., Jomaa, H. S., Wistuba, M., and Grabocka, J · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hyperparameter optimization
Feurer, M. and Hutter, F · 2019
Cited alongside, same era.
An open source automl benchmark
Gijsbers, P., LeDell, E., Poirier, S., Thomas, J., Bischl, B., and Vanschoren, J · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
Cited alongside, same era.
Federated reinforcement learning
Zhuo, H. H., Feng, W., Xu, Q., Yang, Q., and Lin, Y · 2019
Cited alongside, same era.
Fedopt: Towards communication efficiency and privacy preservation in federated learning
Asad, M., Moustafa, A., and Ito, T · 2020
Cited alongside, same era.
Differential evolution for neural architecture search
Awad, N., Mallik, N., and Hutter, F · 2020
Cited alongside, same era.
Federated bayesian optimization via thompson sampling
Dai, Z., Low, B. K. H., and Jaillet, P · 2020
Cited alongside, same era.
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., Data, D., et al · 2021
Later among the works it cites.
Automatic tuning of federated learning hyper-parameters from system perspective
Zhang, H., Zhang, M., Liu, X., Mohapatra, P., and DeLucia, M · 2021
Later among the works it cites.
Flora: Single-shot hyper-parameter optimization for federated learning
Zhou, Y., Ram, P., Salonidis, T., Baracaldo, N., Samulowitz, H., and Ludwig, H · 2021
Later among the works it cites.
Papaya: Practical, private, and scalable federated learning
Huba, D., Nguyen, J., Malik, K., Zhu, R., Rabbat, M., Yousefpour, A., Wu, C.-J., Zhan, H., Ustinov, P., Srinivas, H., et al · 2022
Closest in time.
FedScale: Benchmarking model and system performance of federated learning at scale
Lai, F., Dai, Y., Singapuram, S. S., Liu, J., Zhu, X., Madhyastha, H. V., and Chowdhury, M · 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
Closest in time.
Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization
Pfisterer, F., Schneider, L., Moosbauer, J., Binder, M., and Bischl, B · 2022
Closest in time.
Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning, 2022
Wang, Z., Kuang, W., Xie, Y., Yao, L., Li, Y., Ding, B., and Zhou, J · 2022
Closest in time.
Federatedscope: A flexible federated learning platform for heterogeneity
Xie, Y., Wang, Z., Chen, D., Gao, D., Yao, L., Kuang, W., Li, Y., Ding, B., and Zhou, J · 2022
Closest in time.
Federated hetero-task learning
Yao, L., Gao, D., Wang, Z., Xie, Y., Kuang, W., Chen, D., Wang, H., Dong, C., Ding, B., and Li, Y · 2022
Closest in time.