Fetching the paper…
Reading the bibliography…
We study the problem of optimizing biological sequences, e.g., proteins, DNA, and RNA, to maximize a black-box score function that is only evaluated in an offline dataset.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Design by directed evolution
F. H. Arnold · 1998
Earlier work this paper cites.
Analyzing the effectiveness and applicability of co-training
K. Nigam and R. Ghani · 2000
Earlier work this paper cites.
Semi-supervised logistic regression
M.-R. Amini and P. Gallinari · 2002
Earlier work this paper cites.
Green fluorescent protein (GFP): applications, structure, and related photophysical behavior
M. Zimmer · 2002
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2004
Earlier work this paper cites.
The CMA evolution strategy: a comparing review
N. Hansen · 2006
Earlier work this paper cites.
In the light of directed evolution: pathways of adaptive protein evolution
J. D. Bloom and F. H. Arnold · 2009
Earlier work this paper cites.
Strategy and success for the directed evolution of enzymes
P. A. Dalby · 2011
Earlier work this paper cites.
Levenshtein distance technique in dictionary lookup methods: An improved approach
R. Haldar and D. Mukhopadhyay · 2011
Earlier work this paper cites.
Bootstrap
T. Hesterberg · 2011
Earlier work this paper cites.
Viennarna package 2.0
R. Lorenz, S. H. Bernhart, C. Höner zu Siederdissen, H. Tafer, C. Flamm, P. F. Stadler, and I. L. Hofacker · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
Survey of variation in human transcription factors reveals prevalent dna binding changes
L. A. Barrera, A. Vedenko, J. V. Kurland, J. M. Rogers, S. S. Gisselbrecht, E. J. Rossin, J. Woodard, L. Mariani, K. H. Kock, S. Inukai, et al · 2016
Earlier work this paper cites.
The reparameterization trick for acquisition functions
J. T. Wilson, R. Moriconi, F. Hutter, and M. P. Deisenroth · 2017
Cited alongside, same era.
Directed evolution: bringing new chemistry to life
F. H. Arnold · 2018
Cited alongside, same era.
D. H. Brookes and J. Listgarten · 2018
Cited alongside, same era.
Bayesian optimization for accelerated drug discovery
E. O. Pyzer-Knapp · 2018
Cited alongside, same era.
Model-based reinforcement learning for biological sequence design
C. Angermueller, D. Dohan, D. Belanger, R. Deshpande, K. Murphy, and L. Colwell · 2019
Cited alongside, same era.
Biological sequences design using batched bayesian optimization
Self-training with noisy student improves imagenet classification
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le · 2020
Later among the works it cites.
Flow network based generative models for non-iterative diverse candidate generation
E. Bengio, M. Jain, M. Korablyov, D. Precup, and Y. Bengio · 2021
Later among the works it cites.
Deep extrapolation for attribute-enhanced generation
A. Chan, A. Madani, B. Krause, and N. Naik · 2021
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
Later among the works it cites.
Offline model-based optimization via normalized maximum likelihood estimation
J. Fu and S. Levine · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Belanger, S. Vora, Z. Mariet, R. Deshpande, D. Dohan, C. Angermueller, K. Murphy, O. Chapelle, and L. Colwell · 2019
Cited alongside, same era.
Conditioning by adaptive sampling for robust design
D. Brookes, H. Park, and J. Listgarten · 2019
Cited alongside, same era.
Human 5’ utr design and variant effect prediction from a massively parallel translation assay
P. J. Sample, B. Wang, D. W. Reid, V. Presnyak, I. J. McFadyen, D. R. Morris, and G. Seelig · 2019
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton · 2020
Cited alongside, same era.
Autofocused oracles for model-based design
C. Fannjiang and J. Listgarten · 2020
Cited alongside, same era.
Learning a latent search space for routing problems using variational autoencoders
A. Hottung, B. Bhandari, and K. Tierney · 2020
Cited alongside, same era.
Model inversion networks for model-based optimization
A. Kumar and S. Levine · 2020
Cited alongside, same era.
K. Terayama, M. Sumita, R. Tamura, and K. Tsuda · 2021
Later among the works it cites.
Conservative objective models for effective offline model-based optimization
B. Trabucco, A. Kumar, X. Geng, and S. Levine · 2021
Later among the works it cites.
Roma: Robust model adaptation for offline model-based optimization
S. Yu, S. Ahn, L. Song, and J. Shin · 2021
Later among the works it cites.
Bidirectional learning for offline infinite-width model-based optimization
C. Chen, Y. Zhang, J. Fu, X. Liu, and M. Coates · 2022
Later among the works it cites.
Consistent training via energy-based gflownets for modeling discrete joint distributions
C. Ekbote, M. Jain, P. Das, and Y. Bengio · 2022
Later among the works it cites.
Equivariant 3d-conditional diffusion models for molecular linker design
I. Igashov, H. Stärk, C. Vignac, V. G. Satorras, P. Frossard, M. Welling, M. Bronstein, and B. Correia · 2022
Later among the works it cites.
Biological sequence design with gflownets
M. Jain, E. Bengio, A. Hernandez-Garcia, J. Rector-Brooks, B. F. Dossou, C. A. Ekbote, J. Fu, T. Zhang, M. Kilgour, D. Zhang, et al · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
Later among the works it cites.
Proximal exploration for model-guided protein sequence design
Z. Ren, J. Li, F. Ding, Y. Zhou, J. Ma, and J. Peng · 2022
Later among the works it cites.
Design-bench: Benchmarks for data-driven offline model-based optimization
B. Trabucco, X. Geng, A. Kumar, and S. Levine · 2022
Later among the works it cites.
Importance-aware co-teaching for offline model-based optimization
Y. Yuan, C. Chen, Z. Liu, W. Neiswanger, and X. Liu · 2023
Closest in time.