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For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards.
On a Measure of the Information Provided by an Experiment
Lindley, D. V · 1956
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Evolution strategies for robust optimization
Beyer, H.-G. and Sendhoff, B · 2006
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Elements of Information Theory
Cover, T. M. and Thomas, J. A · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Sequential bayesian optimal experimental design via approximate dynamic programming
Huan, X. and Marzouk, Y. M · 2016
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Hindsight experience replay
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Pieter Abbeel, O., and Zaremba, W · 2017
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Comparison of reinforcement learning algorithms applied to the cart-pole problem
Nagendra, S., Podila, N., Ugarakhod, R., and George, K · 2017
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Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
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Quest+: A general multidimensional bayesian adaptive psychometric method
Watson, A. B · 2017
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Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., Hoof, H., and Meger, D · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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On nesting monte carlo estimators
Rainforth, T., Cornish, R., Yang, H., Warrington, A., and Wood, F · 2018
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Variational bayesian optimal experimental design
Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y. W., Rainforth, T., and Goodman, N · 2019
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Adaptive and safe bayesian optimization in high dimensions via one-dimensional subspaces
Kirschner, J., Mutny, M., Hiller, N., Ischebeck, R., and Krause, A · 2019
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Efficient bayesian experimental design for implicit models
Kleinegesse, S. and Gutmann, M. U · 2019
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On variational bounds of mutual information
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., and Tucker, G · 2019
Cited alongside, same era.
Implementation matters in deep policy gradients: A case study on ppo and trpo
Deep adaptive design: Amortizing sequential bayesian experimental design
Foster, A., Ivanova, D. R., Malik, I., and Rainforth, T · 2021
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Implicit deep adaptive design: Policy-based experimental design without likelihoods
Ivanova, D., Foster, A., Kleinegesse, S., Gutmann, M. U., and Rainforth, T · 2021
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Kleinegesse, S. and Gutmann, M. U · 2021
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Sequential bayesian experimental design for implicit models via mutual information
Kleinegesse, S., Drovandi, C., and Gutmann, M. U · 2021
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ROIAL: Region of interest active learning for characterizing exoskeleton gait preference landscapes
Li, K., Tucker, M., Bıyık, E., Novoseller, E., Burdick, J. W., Sui, Y., Sadigh, D., Yue, Y., and Ames, A. D · 2021
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Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., and Madry, A · 2020
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A unified stochastic gradient approach to designing bayesian-optimal experiments
Foster, A., Jankowiak, M., O’Meara, M., Teh, Y. W., and Rainforth, T · 2020
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Mechanical search on shelves using lateral access x-ray
Huang, H., Dominguez-Kuhne, M., Satish, V., Danielczuk, M., Sanders, K., Ichnowski, J., Lee, A., Angelova, A., Vanhoucke, V., and Goldberg, K · 2020
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BINOCULARS for efficient, nonmyopic sequential experimental design
Jiang, S., Chai, H., Gonzalez, J., and Garnett, R · 2020
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Bayesian experimental design for implicit models by mutual information neural estimation
Kleinegesse, S. and Gutmann, M. U · 2020
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Understanding the limitations of variational mutual information estimators
Song, J. and Ermon, S · 2020
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Mirrored plasmonic filter design via active learning of multi-fidelity physical models
Song, J., Tokpanov, Y. S., Chen, Y., Fleischman, D., Fountaine, K. T., Yue, Y., and Atwater, H. A · 2020
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Planar robot casting with real2sim2real self-supervised learning, 2021
Lim, V., Huang, H., Chen, L. Y., Wang, J., Ichnowski, J., Seita, D., Laskey, M., and Goldberg, K · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Makoviychuk, V., Wawrzyniak, L., Guo, Y., Lu, M., Storey, K., Macklin, M., Hoeller, D., Rudin, N., Allshire, A., Handa, A., et al · 2021
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Shen, W. and Huan, X · 2021
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Machine learning-assisted directed evolution navigates a combinatorial epistatic fitness landscape with minimal screening burden
Wittmann, B. J., Yue, Y., and Arnold, F. H · 2021
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End-to-end sequential sampling and reconstruction for mri
Yin, T., Wu, Z., Sun, H., Dalca, A. V., Yue, Y., and Bouman, K. L · 2021
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A scalable gradient free method for bayesian experimental design with implicit models
Zhang, J., Bi, S., and Zhang, G · 2021
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Mutual information state intrinsic control
Zhao, R., Gao, Y., Abbeel, P., Tresp, V., and Xu, W · 2021
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Optimizing sequential experimental design with deep reinforcement learning
Blau, T., Bonilla, E., Dezfouli, A., and Chades, I · 2022
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