Fetching the paper…
Reading the bibliography…
In this work, we aim to solve data-driven optimization problems, where the goal is to find an input that maximizes an unknown score function given access to a dataset of inputs with corresponding scores.
Data-efficient learning of morphology and controller for a microrobot
Liao, T., Wang, G., Yang, B., Lee, R., Pister, K., Levine, S., and Calandra, R · 1905
Earlier work this paper cites.
A tutorial on thompson sampling
Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., and Wen, Z · 1935
Earlier work this paper cites.
Optimization of computer simulation models with rare events
Rubinstein, R. Y · 1996
Earlier work this paper cites.
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Rubinstein, R. Y. and Kroese, D. P · 2004
Earlier work this paper cites.
Reinforcement learning by reward-weighted regression for operational space control
Peters, J. and Schaal, S · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 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.
Geometric programming for aircraft design optimization
Hoburg, W. and Abbeel, P · 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.
Auto-encoding variational bayes, 2013
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Eluder dimension and the sample complexity of optimistic exploration
Russo, D. and Van Roy, B · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Conditional generative adversarial nets, 2014
Mirza, M. and Osindero, S · 2014
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Cited alongside, same era.
Dex: Deep expectation of apparent age from a single image
Rothe, R., Timofte, R., and Gool, L. V · 2015
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.
Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T. B., Martic, M., Legg, S., and Amodei, D · 2017
Later among the works it cites.
Seqgan: Sequence generative adversarial nets with policy gradient
Yu, L., Zhang, W., Wang, J., and Yu, Y · 2017
Later among the works it cites.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Later among the works it cites.
Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Duvenaud, D., Hernández-Lobato, J. M., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
Later among the works it cites.
Feedback gan (fbgan) for dna: a novel feedback-loop architecture for optimizing protein functions
Gupta, A. and Zou, J · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
Cited alongside, same era.
Deep expectation of real and apparent age from a single image without facial landmarks
Rothe, R., Timofte, R., and Gool, L. V · 2016
Cited alongside, same era.
An information-theoretic analysis of thompson sampling
Russo, D. and Van Roy, B · 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
Cited alongside, same era.
Pixel recurrent neural networks
Van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Generative visual manipulation on the natural image manifold
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., and Efros, A. A · 2016
Cited alongside, same era.
Deep learning with logged bandit feedback
Joachims, T., Swaminathan, A., and de Rijke, M · 2018
Later among the works it cites.
Policy optimization via importance sampling
Metelli, A. M., Papini, M., Faccio, F., and Restelli, M · 2018
Later among the works it cites.
Parallel WaveNet: Fast high-fidelity speech synthesis
van den Oord, A., Li, Y., Babuschkin, I., and et.al · 2018
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Closest in time.
Conditioning by adaptive sampling for robust design
Brookes, D., Park, H., and Listgarten, J · 2019
Closest in time.
Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
Closest in time.
Variational discriminator bottleneck: Improving imitation learning, inverse RL, and GANs by constraining information flow
Peng, X. B., Kanazawa, A., Toyer, S., Abbeel, P., and Levine, S · 2019
Closest in time.