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Optimizing discrete black-box functions is key in several domains, e.g.
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Information-theoretic regret bounds for gaussian process optimization in the bandit setting
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High-dimensional gaussian process bandits
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Active learning of linear embeddings for gaussian processes
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Bayesian optimization in high dimensions via random embeddings
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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) · 2014
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Auto-encoding variational bayes
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Stochastic backpropagation and approximate inference in deep generative models
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A warped kernel improving robustness in bayesian optimization via random embeddings
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Geodesic Exponential Kernels: When Curvature and Linearity Conflict
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Do we need “harmless” bayesian optimization and “first-order” bayesian optimization
Ahmed, M. O., Shahriari, B., and Schmidt, M. (2016) · 2016
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High dimensional bayesian optimization via restricted projection pursuit models
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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. (2016) · 2016
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Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F. (2016) · 2016
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Bayesian optimization with dimension scheduling: Application to biological systems
Ulmasov, D., Baroukh, C., Chachuat, B., Deisenroth, M. P., and Misener, R. (2016) · 2016
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Bayesian optimization in a billion dimensions via random embeddings
Wang, Z., Hutter, F., Zoghi, M., Matheson, D., and De Feitas, N. (2016) · 2016
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Discovering and Exploiting Additive Structure for Bayesian Optimization
Gardner, J., Guo, C., Weinberger, K., Garnett, R., and Grosse, R. (2017) · 2017
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GPflow: A Gaussian process library using TensorFlow
Matthews, A. G. d. G., van der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z., and Hensman, J. (2017) · 2017
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Exploiting active subspaces in global optimization: how complex is your problem?
Palar, P. S. and Shimoyama, K. (2017) · 2017
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High dimensional Bayesian optimization with elastic Gaussian process
Rana, S., Li, C., Gupta, S., Nguyen, V., and Venkatesh, S. (2017) · 2017
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Bayesian optimization with gradients
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Bayesian optimization of combinatorial structures
Baptista, R. and Poloczek, M. (2018) · 2018
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Bayesian optimization and attribute adjustment
Eissman, S., Levy, D., Shu, R., Bartzsch, S., and Ermon, S. (2018) · 2018
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Scaling gaussian process regression with derivatives
Eriksson, D., Dong, K., Lee, E., Bindel, D., and Wilson, A. G. (2018) · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G. (2018) · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A. (2018) · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T. (2018) · 2018
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High dimensional bayesian optimization using dropout
Li, C., Gupta, S., Rana, S., Nguyen, V., Venkatesh, S., and Shilton, A. (2018) · 2018
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Structured variationally auto-encoded optimization
Lu, X., Gonzalez, J., Dai, Z., and Lawrence, N. (2018) · 2018
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Efficient high dimensional bayesian optimization with additivity and quadrature fourier features
Mutny, M. and Krause, A. (2018) · 2018
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BOCK : Bayesian optimization with cylindrical kernels
Oh, C., Gavves, E., and Welling, M. (2018) · 2018
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Bayesian optimization for accelerated drug discovery
Pyzer-Knapp, E. O. (2018) · 2018
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High-dimensional bayesian optimization via additive models with overlapping groups
Rolland, P., Scarlett, J., Bogunovic, I., and Cevher, V. (2018) · 2018
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Batched large-scale bayesian optimization in high-dimensional spaces
Wang, Z., Gehring, C., Kohli, P., and Jegelka, S. (2018) · 2018
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Active manifolds: A non-linear analogue to active subspaces
Bridges, R. A., Gruber, A. D., Felder, C., Verma, M., and Hoff, C. (2019) · 2019
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C. (2019) · 2019
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FoldX 5.0: working with RNA, small molecules and a new graphical interface
Delgado, J., Radusky, L. G., Cianferoni, D., and Serrano, L. (2019) · 2019
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Scalable global optimization via local bayesian optimization
Hebo: Pushing the limits of sample-efficient hyper-parameter optimisation
Cowen-Rivers, A. I., Lyu, W., Tutunov, R., Wang, Z., Grosnit, A., Griffiths, R. R., Maraval, A. M., Jianye, H., Wang, J., Peters, J., et al. (2022) · 2022
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Sample efficiency matters: A benchmark for practical molecular optimization
Gao, W., Fu, T., Sun, J., and Coley, C. (2022) · 2022
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Dockstring: easy molecular docking yields better benchmarks for ligand design
García-Ortegón, M., Simm, G. N., Tripp, A. J., Hernández-Lobato, J. M., Bender, A., and Bacallado, S. (2022) · 2022
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Linear embedding-based high-dimensional batch bayesian optimization without reconstruction mappings
Horiguchi, S. A., Iwata, T., Tsuzuki, T., and Ozawa, Y. (2022) · 2022
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Combinatorial bayesian optimization with random mapping functions to convex polytopes
Kim, J., Choi, S., and Cho, M. (2022) · 2022
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Eriksson, D., Pearce, M., Gardner, J., Turner, R. D., and Poloczek, M. (2019) · 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) · 2019
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A framework for Bayesian optimization in embedded subspaces
Nayebi, A., Munteanu, A., and Poloczek, M. (2019) · 2019
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Combinatorial bayesian optimization using the graph cartesian product
Oh, C., Tomczak, J., Gavves, E., and Welling, M. (2019) · 2019
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High dimensional bayesian optimization via supervised dimension reduction
Zhang, M., Li, H., and Su, S. (2019) · 2019
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Botorch: A framework for efficient monte-carlo bayesian optimization
Balandat, M., Karrer, B., Jiang, D., Daulton, S., Letham, B., Wilson, A. G., and Bakshy, E. (2020) · 2020
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On the choice of the low-dimensional domain for global optimization via random embeddings
Binois, M., Ginsbourger, D., and Roustant, O. (2020) · 2020
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Local latent space bayesian optimization over structured inputs
Maus, N., Jones, H., Moore, J., Kusner, M. J., Bradshaw, J., and Gardner, J. (2022) · 2022
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Local bayesian optimization via maximizing probability of descent
Nguyen, Q., Wu, K., Gardner, J., and Garnett, R. (2022) · 2022
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Increasing the scope as you learn: Adaptive bayesian optimization in nested subspaces
Papenmeier, L., Nardi, L., and Poloczek, M. (2022) · 2022
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Protein geometry, function and mutation
Penner, R. (2022) · 2022
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Monte carlo tree search based variable selection for high dimensional bayesian optimization
Song, L., Xue, K., Huang, X., and Qian, C. (2022) · 2022
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Accelerating Bayesian optimization for biological sequence design with denoising autoencoders
Stanton, S., Maddox, W., Gruver, N., Maffettone, P., Delaney, E., Greenside, P., and Wilson, A. G. (2022) · 2022
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Dual use of artificial-intelligence-powered drug discovery
Urbina, F., Lentzos, F., Invernizzi, C., and Ekins, S. (2022) · 2022
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Highdimensional bayesian optimization with invariance
Verma, E., Chakraborty, S., and Griffiths, R.-R. (2022) · 2022
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Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhu, Z., Shi, C., Zhang, Z., Liu, S., Xu, M., Yuan, X., Zhang, Y., Chen, J., Cai, H., Lu, J., Ma, C., Liu, R., Xhonneux, L.-P., Qu, M., and Tang, J. (2022) · 2022
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Rapid protein stability prediction using deep learning representations
Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., and Lindorff-Larsen, K. (2023) · 2023
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An introduction to optimization on smooth manifolds
Boumal, N. (2023) · 2023
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Bayesian optimization over high-dimensional combinatorial spaces via dictionary-based embeddings
Deshwal, A., Ament, S., Balandat, M., Bakshy, E., Doppa, J. R., and Eriksson, D. (2023) · 2023
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Trego: a trust-region framework for efficient global optimization
Diouane, Y., Picheny, V., Riche, R. L., and Perrotolo, A. S. D. (2023) · 2023
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Framework and benchmarks for combinatorial and mixed-variable bayesian optimization
Dreczkowski, K., Grosnit, A., and Ammar, H. B. (2023) · 2023
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Bayesian Optimization
Garnett, R. (2023) · 2023
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Gauche: A library for gaussian processes in chemistry
Griffiths, R.-R., Klarner, L., Moss, H., Ravuri, A., Truong, S., Du, Y., Stanton, S., Tom, G., Rankovic, B., Jamasb, A., Deshwal, A., Schwartz, J., Tripp, A., Kell, G., Frieder, S., Bourached, A., Chan, A., Moss, J., Guo, C., Dürholt, J. P., Chaurasia, S., Park, J. W., Strieth-Kalthoff, F., Lee, A., Cheng, B., Aspuru-Guzik, A., Schwaller, P., and Tang, J. (2023) · 2023
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Protein design with guided discrete diffusion
Gruver, N., Stanton, S., Frey, N., Rudner, T. G. J., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G. (2023) · 2023
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High-dimensional bayesian optimization with group testing
Hellsten, E. O., Hvarfner, C., Papenmeier, L., and Nardi, L. (2023) · 2023
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Self-correcting bayesian optimization through bayesian active learning
Hvarfner, C., Hellsten, E., Hutter, F., and Nardi, L. (2023) · 2023
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Toward real-world automated antibody design with combinatorial bayesian optimization
Khan, A., Cowen-Rivers, A. I., Grosnit, A., Robert, P. A., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., Tutunov, R., et al. (2023) · 2023
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Advancing bayesian optimization via learning correlated latent space
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Discovering many diverse solutions with bayesian optimization
Maus, N., Wu, K., Eriksson, D., and Gardner, J. (2023) · 2023
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
Notin, P., Kollasch, A., Ritter, D., van Niekerk, L., Paul, S., Spinner, H., Rollins, N., Shaw, A., Orenbuch, R., Weitzman, R., Frazer, J., Dias, M., Franceschi, D., Gal, Y., and Marks, D. (2023) · 2023
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Trieste: Efficiently exploring the depths of black-box functions with tensorflow
Picheny, V., Berkeley, J., Moss, H. B., Stojic, H., Granta, U., Ober, S. W., Artemev, A., Ghani, K., Goodall, A., Paleyes, A., Vakili, S., Pascual-Diaz, S., Markou, S., Qing, J., Loka, N. R. B. S., and Couckuyt, I. (2023) · 2023
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