Fetching the paper…
Reading the bibliography…
Neural architecture search (NAS) automates the design of deep neural networks.
On scalable variant of wasserstein barycenter
Le, T., Huynh, V., Ho, N., Phung, D., and Yamada, M · 1910
Earlier work this paper cites.
Harmonic analysis on semigroups
Berg, C., Christensen, J. P. R., and Ressel, P · 1984
Earlier work this paper cites.
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Back, T · 1996
Earlier work this paper cites.
A new algorithm for error-tolerant subgraph isomorphism detection
Messmer, B. T. and Bunke, H · 1998
Earlier work this paper cites.
Linear assignment problems and extensions
Burkard, R. E. and Cela, E · 1999
Earlier work this paper cites.
Graph distances using graph union
Wallis, W. D., Shoubridge, P., Kraetz, M., and Ray, D · 2001
Earlier work this paper cites.
Diffusion kernels on graphs and other discrete input spaces
Kondor, R. and Lafferty, J · 2002
Earlier work this paper cites.
Phylogenetics
Semple, C. and Steel, M · 2003
Earlier work this paper cites.
Kernels and regularization on graphs
Smola, A. J. and Kondor, R · 2003
Earlier work this paper cites.
Topics in optimal transportation
Villani, C · 2003
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E · 2006
Earlier work this paper cites.
Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
Earlier work this paper cites.
A survey of graph edit distance
Gao, X., Xiao, B., Tao, D., and Li, X · 2010
Earlier work this paper cites.
Bayesian optimization for sensor set selection
Garnett, R., Osborne, M. A., and Roberts, S. J · 2010
Earlier work this paper cites.
Kriging is well-suited to parallelize optimization
Ginsbourger, D., Le Riche, R., and Carraro, L · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M · 2010
Earlier work this paper cites.
Graph kernels
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., and Borgwardt, K. M · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B · 2011
Earlier work this paper cites.
Sublinear time algorithms for Earth Mover’s distance
Do Ba, K., Nguyen, H. L., Nguyen, H. N., and Rubinfeld, R · 2011
Earlier work this paper cites.
k-dpps: Fixed-size determinantal point processes
Kulesza, A. and Taskar, B · 2011
Earlier work this paper cites.
Gromov–wasserstein distances and the metric approach to object matching
Mémoli, F · 2011
Earlier work this paper cites.
Determinantal point processes for machine learning
Kulesza, A., Taskar, B., et al · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
Earlier work this paper cites.
Parallel gaussian process optimization with upper confidence bound and pure exploration
Contal, E., Buffoni, D., Robicquet, A., and Vayatis, N · 2013
Earlier work this paper cites.
Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization
Desautels, T., Krause, A., and Burdick, J. W · 2014
Cited alongside, same era.
Scalable Bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
Cited alongside, same era.
Batch Bayesian optimization via local penalization
González, J., Dai, Z., Hennig, P., and Lawrence, N. D · 2016
Cited alongside, same era.
Batched Gaussian process bandit optimization via determinantal point processes
Kathuria, T., Deshpande, A., and Kohli, P · 2016
Cited alongside, same era.
Budgeted batch Bayesian optimization
Nguyen, V., Rana, S., Gupta, S. K., Li, C., and Venkatesh, S · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2016
Amoebanet: An sdn-enabled network service for big data science
Shah, S. A. R., Wu, W., Lu, Q., Zhang, L., Sasidharan, S., DeMar, P., Guok, C., Macauley, J., Pouyoul, E., Kim, J., et al · 2018
Later among the works it cites.
Batched large-scale Bayesian optimization in high-dimensional spaces
Wang, Z., Gehring, C., Kohli, P., and Jegelka, S · 2018
Later among the works it cites.
Practical block-wise neural network architecture generation
Zhong, Z., Yan, J., Wu, W., Shao, J., and Liu, C.-L · 2018
Later among the works it cites.
Searching for a robust neural architecture in four gpu hours
Dong, X. and Yang, Y · 2019
Later among the works it cites.
Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
Later among the works it cites.
Gromov-hausdorff distances on p p -metric spaces and ultrametric spaces
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F · 2016
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2017
Cited alongside, same era.
Pot python optimal transport library
Flamary, R. and Courty, N · 2017
Cited alongside, same era.
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
Hernández-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O., and Aspuru-Guzik, A · 2017
Cited alongside, same era.
High dimensional Bayesian optimization with elastic Gaussian process
Rana, S., Li, C., Gupta, S., Nguyen, V., and Venkatesh, S · 2017
Cited alongside, same era.
Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Tan, J., Le, Q. V., and Kurakin, A · 2017
Cited alongside, same era.
Mémoli, F., Smith, Z., and Wan, Z · 2019
Later among the works it cites.
Computational optimal transport
Peyré, G. and Cuturi, M · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
Later among the works it cites.
Evaluating the search phase of neural architecture search
Sciuto, C., Yu, K., Jaggi, M., Musat, C., and Salzmann, M · 2019
Later among the works it cites.
Snas: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2019
Later among the works it cites.
Nas-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., and Hutter, F · 2019
Later among the works it cites.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Dong, X. and Yang, Y · 2020
Closest in time.
Knowing the what but not the where in Bayesian optimization
Nguyen, V. and Osborne, M. A · 2020
Closest in time.
Bayesian optimization for iterative learning
Nguyen, V., Schulze, S., and Osborne, M. A · 2020
Closest in time.
Provably efficient online hyperparameter optimization with population-based bandits
Parker-Holder, J., Nguyen, V., and Roberts, S. J · 2020
Closest in time.
Bayesian optimisation over multiple continuous and categorical inputs
Ru, B., Alvi, A. S., Nguyen, V., Osborne, M. A., and Roberts, S. J · 2020
Closest in time.
Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Wang, L., Zhao, Y., Jinnai, Y., Tian, Y., and Fonseca, R · 2020
Closest in time.
Efficient neural architecture search via proximal iterations
Yao, Q., Xu, J., Tu, W.-W., and Zhu, Z · 2020
Closest in time.
Entropy partial transport with tree metrics: Theory and practice
Le, T. and Nguyen, T · 2021
Closest in time.
Flow-based alignment approaches for probability measures in different spaces
Le, T., Ho, N., and Yamada, M · 2021
Closest in time.
The ultrametric gromov-wasserstein distance
Mémoli, F., Munk, A., Wan, Z., and Weitkamp, C · 2021
Closest in time.
Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
Ru, B., Wan, X., Dong, X., and Osborne, M. A · 2021
Closest in time.
Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Wan, X., Nguyen, V., Ha, H., Ru, B., Lu, C., and Osborne, M. A · 2021
Closest in time.
Bananas: Bayesian optimization with neural architectures for neural architecture search
White, C., Neiswanger, W., and Savani, Y · 2021
Closest in time.