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The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations.
Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D., Singh, S., and Mansour, Y · 1999
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The cma evolution strategy: a comparing review
Hansen, N · 2006
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Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M. W · 2010
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
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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 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
Swaminathan, A. and Joachims, T · 2015
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On explore-then-commit strategies
Garivier, A., Kaufmann, E., and Lattimore, T · 2016
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The cma evolution strategy: A tutorial
Hansen, N · 2016
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Best arm identification in multi-armed bandits with delayed feedback
Grover, A., Markov, T., Attia, P., Jin, N., Perkins, N., Cheong, B., Chen, M., Yang, Z., Harris, S., Chueh, W., and Ermon, S · 2018
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Deep learning with logged bandit feedback
Joachims, T., Swaminathan, A., and de Rijke, M · 2018
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Riquelme, C., Tucker, G., and Snoek, J · 2018
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Conditioning by adaptive sampling for robust design
Brookes, D., Park, H., and Listgarten, J · 2019
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Closed-loop optimization of extreme fast charging for batteries using machine learning
Attia, P., Grover, A., Jin, N., Severson, K., Cheong, B., Liao, J., Chen, M. H., Perkins, N., Yang, Z., Herring, P., Aykol, M., Harris, S., Braatz, R., Ermon, S., and Chueh, W · 2020
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Autofocused oracles for model-based design
Fannjiang, C. and Listgarten, J · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Model inversion networks for model-based optimization
Kumar, A. and Levine, S · 2020
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Video diffusion models
Ho, J., Salimans, T., Gritsenko, A. A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
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Hoogeboom, E. and Salimans, T · 2022
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Planning with diffusion for flexible behavior synthesis
Janner, M., Du, Y., Tenenbaum, J. B., and Levine, S · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Guided-tts: A diffusion model for text-to-speech via classifier guidance
Kim, H., Kim, S., and Yoon, S · 2022
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Dhariwal, P. and Nichol, A. Q · 2021
Cited alongside, same era.
Learning from an exploring demonstrator: Optimal reward estimation for bandits
Guo, W., Agrawal, K. K., Grover, A., Muthukumar, V., and Pananjady, A · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
Cited alongside, same era.
A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A. C · 2021
Cited alongside, same era.
Diff-tts: A denoising diffusion model for text-to-speech
Jeong, M., Kim, H., Cheon, S. J., Choi, B. J., and Kim, N. S · 2021
Cited alongside, same era.
Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2021
Cited alongside, same era.
Masked autoencoding for scalable and generalizable decision making
Liu, F., Liu, H., Grover, A., and Abbeel, P · 2022
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Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
Nguyen, T. and Grover, A · 2022
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Reliable conditioning of behavioral cloning for offline reinforcement learning
Nguyen, T., Zheng, Q., and Grover, A · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
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Design-bench: Benchmarks for data-driven offline model-based optimization
Trabucco, B., Geng, X., Kumar, A., and Levine, S · 2022
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Unifying likelihood-free inference with black-box sequence design and beyond
Zhang, D., Fu, J., Bengio, Y., and Courville, A · 2022
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Online decision transformer
Zheng, Q., Zhang, A., and Grover, A · 2022
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Leaving reality to imagination: Robust classification via generated datasets
Bansal, H. and Grover, A · 2023
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Generative pretraining for black-box optimization
Krishnamoorthy, S., Mashkaria, S. M., and Grover, A · 2023
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Semi-supervised offline reinforcement learning with action-free trajectories
Zheng, Q., Henaff, M., Amos, B., and Grover, A · 2023
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Scaling pareto-efficient decision making via offline multi-objective rl
Zhu, B., Dang, M., and Grover, A · 2023
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