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We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time.
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A sequential monte carlo algorithm to incorporate model uncertainty in bayesian sequential design
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Auto-encoding variational Bayes
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Inference suboptimality in variational autoencoders
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On nesting monte carlo estimators
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Representation learning with contrastive predictive coding
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Zheng, S., Pacheco, J., and Fisher, J · 2018
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Measuring delay discounting in humans using an adjusting amount task
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Glasses: Relieving the myopia of bayesian optimisation
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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On variational bounds of mutual information
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Dushenko, S., Ambal, K., and McMichael, R. D · 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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Unbiased mlmc stochastic gradient-based optimization of bayesian experimental designs
Goda, T., Hironaka, T., and Kitade, W · 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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Sequential bayesian experimental design for implicit models via mutual information
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A laplace-based algorithm for bayesian adaptive design
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Sequential bayesian experimental design with variable cost structure
Zheng, S., Hayden, D., Pacheco, J., and Fisher III, J. W · 2020
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