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Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging.
Some aspects of the sequential design of experiments
Robbins, H · 1952
Earlier work this paper cites.
On a measure of the information provided by an experiment
Lindley, D. V · 1956
Earlier work this paper cites.
A markovian decision process
Bellman, R · 1957
Earlier work this paper cites.
Bayesian experimental design: A review
Chaloner, K. and Verdinelli, I · 1995
Earlier work this paper cites.
Bayesian design of experiments for the linear model
Verdinelli, I · 1996
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D., Singh, S., and Mansour, Y · 1999
Earlier work this paper cites.
Optimal bayesian design by inhomogeneous markov chain simulation
Müller, P., Sansó, B., and De Iorio, M · 2004
Earlier work this paper cites.
Reinforcement learning or active inference?
Friston, K. J., Daunizeau, J., and Kiebel, S. J · 2009
Earlier work this paper cites.
Estimating the mean of a non-linear function of conditional expectation
Hong, L. J. and Juneja, S · 2009
Earlier work this paper cites.
Optimal experimental design for model discrimination
Myung, J. I. and Pitt, M. A · 2009
Earlier work this paper cites.
Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science
Cavagnaro, D. R., Myung, J. I., Pitt, M. A., and Kujala, J. V · 2010
Earlier work this paper cites.
Planning to be surprised: Optimal bayesian exploration in dynamic environments
Sun, Y., Gomez, F., and Schmidhuber, J · 2011
Earlier work this paper cites.
Simulation-based optimal bayesian experimental design for nonlinear systems
Huan, X. and Marzouk, Y. M · 2013
Earlier work this paper cites.
A tutorial on adaptive design optimization
Myung, J. I., Cavagnaro, D. R., and Pitt, M. A · 2013
Earlier work this paper cites.
A sequential monte carlo algorithm to incorporate model uncertainty in bayesian sequential design
Drovandi, C. C., McGree, J. M., and Pettitt, A. N · 2014
Earlier work this paper cites.
Sample-based search methods for Bayes-adaptive planning
Guez, A · 2015
Earlier work this paper cites.
Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations
Doshi-Velez, F. and Konidaris, G · 2016
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Vime: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P · 2016
Cited alongside, same era.
Sequential bayesian optimal experimental design via approximate dynamic programming
Huan, X. and Marzouk, Y. M · 2016
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A review of modern computational algorithms for Bayesian optimal design
Ryan, E. G., Drovandi, C. C., McGree, J. M., and Pettitt, A. N · 2016
Cited alongside, same era.
Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Multilevel monte carlo estimation of expected information gains
Goda, T., Hironaka, T., and Iwamoto, T · 2020
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Bayesian optimization for adaptive experimental design: A review
Greenhill, S., Rana, S., Gupta, S., Vellanki, P., and Venkatesh, S · 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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Curl: Contrastive unsupervised representations for reinforcement learning
Laskin, M., Srinivas, A., and Abbeel, P · 2020
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Sequential experimental design for predator–prey functional response experiments
Moffat, H., Hainy, M., Papanikolaou, N. E., and Drovandi, C · 2020
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Exploration strategies in deep reinforcement learning
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Fast bayesian experimental design: Laplace-based importance sampling for the expected information gain
Beck, J., Dia, B. M., Espath, L. F., Long, Q., and Tempone, R · 2018
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Pyro: Deep Universal Probabilistic Programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D · 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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Garage: A toolkit for reproducible reinforcement learning research
Garage Contributors · 2019
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Shaping belief states with generative environment models for rl
Gregor, K., Jimenez Rezende, D., Besse, F., Wu, Y., Merzic, H., and van den Oord, A · 2019
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Weng, L · 2020
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Randomized ensembled double q-learning: Learning fast without a model
Chen, X., Wang, C., Zhou, Z., and Ross, K. W · 2021
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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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Sequential bayesian experimental design for implicit models via mutual information
Kleinegesse, S., Drovandi, C., and Gutmann, M. U · 2021
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Surrogate-based sequential bayesian experimental design using non-stationary gaussian processes
Pandita, P., Tsilifis, P., Awalgaonkar, N. M., Bilionis, I., and Panchal, J · 2021
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Shen, W. and Huan, X · 2021
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Made: Exploration via maximizing deviation from explored regions
Zhang, T., Rashidinejad, P., Jiao, J., Tian, Y., Gonzalez, J. E., and Russell, S · 2021
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Variational, Monte Carlo and Policy-Based Approaches to Bayesian Experimental Design
Foster, A · 2022
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Exploration in deep reinforcement learning: A survey
Ladosz, P., Weng, L., Kim, M., and Oh, H · 2022
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Properties of fisher information gain for bayesian design of experiments
Overstall, A. M · 2022
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