Fetching the paper…
Reading the bibliography…
Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores.
The CMA evolution strategy: a comparing review
Hansen, N · 2006
Earlier work this paper cites.
Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis
Starita, L. M., Pruneda, J. N., Lo, R. S., Fowler, D. M., Kim, H. J., Hiatt, J. B., Shendure, J., Brzovic, P. S., Fields, S., and Klevit, R. E · 2013
Earlier work this paper cites.
Adam: a method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning
Maclaurin, D., Duvenaud, D., and Adams, R · 2015
Earlier work this paper cites.
Survey of variation in human transcription factors reveals prevalent DNA binding changes
Barrera, L. A. et al · 2016
Earlier work this paper cites.
Scalable gradient-based tuning of continuous regularization hyperparameters
Luketina, J., Berglund, M., Greff, K., and Raiko, T · 2016
Earlier work this paper cites.
Hyperparameter optimization with approximate gradient
Pedregosa, F · 2016
Earlier work this paper cites.
Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S. et al · 2016
Earlier work this paper cites.
Online learning rate adaptation with hypergradient descent
Baydin, A. G., Cornish, R., Rubio, D. M., Schmidt, M., and Wood, F · 2017
Earlier work this paper cites.
Forward and reverse gradient-based hyperparameter optimization
Franceschi, L., Donini, M., Frasconi, P., and Pontil, M · 2017
Earlier work this paper cites.
Generating and designing DNA with deep generative models
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., and Frey, B. J · 2017
Earlier work this paper cites.
The reparameterization trick for acquisition functions
Wilson, J. T., Moriconi, R., Hutter, F., and Deisenroth, M. P · 2017
Earlier work this paper cites.
Bilevel programming for hyperparameter optimization and meta-learning
Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., and Pontil, M · 2018
Earlier work this paper cites.
Co-teaching: robust training of deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 2018
Earlier work this paper cites.
Model-based reinforcement learning for biological sequence design
Angermueller, C., Dohan, D., Belanger, D., Deshpande, R., Murphy, K., and Colwell, L · 2019
Earlier work this paper cites.
Conditioning by adaptive sampling for robust design
Brookes, D., Park, H., and Listgarten, J · 2019
Earlier work this paper cites.
Marthe: Scheduling the learning rate via online hypergradients
Donini, M., Franceschi, L., Pontil, M., Majumder, O., and Frasconi, P · 2019
Earlier work this paper cites.
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., et al · 2019
Cited alongside, same era.
Harnessing the power of infinitely wide deep nets on small-data tasks
Arora, S., Du, S. S., Li, Z., Salakhutdinov, R., Wang, R., and Yu, D · 2020
Cited alongside, same era.
Fast differentiable DNA and protein sequence optimization for molecular design
Linder, J. and Seelig, G · 2020
Cited alongside, same era.
Optimizing millions of hyperparameters by implicit differentiation
Lorraine, J., Vicol, P., and Duvenaud, D · 2020
Cited alongside, same era.
Adalead: a simple and robust adaptive greedy search algorithm for sequence design
Sinai, S., Wang, R., Whatley, A., Slocum, S., Locane, E., and Kelsic, E. D · 2020
Cited alongside, same era.
Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Liu, R., Gao, J., Zhang, J., Meng, D., and Lin, Z · 2021
Later among the works it cites.
Gradient-based hyperparameter optimization over long horizons
Micaelli, P. and Storkey, A. J · 2021
Later among the works it cites.
Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Baker, D., and Ovchinnikov, S · 2021
Later among the works it cites.
Black-box optimization for automated discovery
Terayama, K., Sumita, M., Tamura, R., and Tsuda, K · 2021
Later among the works it cites.
Conservative objective models for effective offline model-based optimization
Trabucco, B., Kumar, A., Geng, X., and Levine, S · 2021
Later among the works it cites.
Tensor programs iv: feature learning in infinite-width neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Design of proteins presenting discontinuous functional sites using deep learning
Tischer, D., Lisanza, S., Wang, J., Dong, R., Anishchenko, I., Milles, L. F., Ovchinnikov, S., and Baker, D · 2020
Cited alongside, same era.
Lqf: linear quadratic fine-tuning
Achille, A., Golatkar, A., Ravichandran, A., Polito, M., and Soatto, S · 2021
Cited alongside, same era.
Evograd: Efficient gradient-based meta-learning and hyperparameter optimization
Bohdal, O., Yang, Y., and Hospedales, T · 2021
Cited alongside, same era.
Deep diversification of an AAV capsid protein by machine learning
Bryant, D. H., Bashir, A., Sinai, S., Jain, N. K., Ogden, P. J., Riley, P. F., Church, G. M., Colwell, L. J., and Kelsic, E. D · 2021
Cited alongside, same era.
Deep extrapolation for attribute-enhanced generation
Chan, A., Madani, A., Krause, B., and Naik, N · 2021
Cited alongside, same era.
Generalized data weighting via class-level gradient manipulation
Chen, C., Zheng, S., Chen, X., Dong, E., Liu, X. S., Liu, H., and Dou, D · 2021
Cited alongside, same era.
ProtTrans: towards cracking the language of lifes code through self-supervised deep learning and high performance computing
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al · 2021
Cited alongside, same era.
Yang, G. and Hu, E. J · 2021
Later among the works it cites.
Roma: robust model adaptation for offline model-based optimization
Yu, S., Ahn, S., Song, L., and Shin, J · 2021
Later among the works it cites.
Towards good validation metrics for generative models in offline model-based optimisation
Beckham, C., Piche, A., Vazquez, D., and Pal, C · 2022
Later among the works it cites.
DIVA: Dataset derivative of a learning task
Dukler, Y., Achille, A., Paolini, G., Ravichandran, A., Polito, M., and Soatto, S · 2022
Later among the works it cites.
Biological sequence design with GFlowNets
Jain, M., Bengio, E., Hernandez-Garcia, A., Rector-Brooks, J., Dossou, B. F., Ekbote, C. A., Fu, J., Zhang, T., Kilgour, M., Zhang, D., et al · 2022
Later among the works it cites.
Data-driven optimization for protein design: workflows, algorithms and metrics
Kolli, S., Lu, A. X., Geng, X., Kumar, A., and Levine, S · 2022
Later among the works it cites.
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis
Murray, C. J., Ikuta, K. S., Sharara, F., Swetschinski, L., Aguilar, G. R., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., et al · 2022
Later among the works it cites.
Proximal exploration for model-guided protein sequence design
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., and Peng, J · 2022
Later among the works it cites.
Design-Bench: benchmarks for data-driven offline model-based optimization
Trabucco, B., Geng, X., Kumar, A., and Levine, S · 2022
Later among the works it cites.
On implicit bias in overparameterized bilevel optimization
Vicol, P., Lorraine, J. P., Pedregosa, F., Duvenaud, D., and Grosse, R. B · 2022
Later among the works it cites.
ProtFIM: Fill-in-middle protein sequence design via protein language models, 2023
Lee, Y. and Yu, H · 2023
Closest in time.