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Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design, among others.
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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The reparameterization trick for acquisition functions
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Yixin Wang, Alp Kucukelbir, and David M Blei · 2017
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Kam Hamidieh · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Data-efficient learning of morphology and controller for a microrobot
Thomas Liao, Grant Wang, Brian Yang, Rene Lee, Kristofer Pister, Sergey Levine, and Roberto Calandra · 2019
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Model-based reinforcement learning for biological sequence design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, and Lucy Colwell · 2019
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Human 5 UTR design and variant effect prediction from a massively parallel translation assay
Paul J Sample, Ban Wang, David W Reid, Vlad Presnyak, Iain J McFadyen, David R Morris, and Georg Seelig · 2019
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Test-time fast adaptation for dynamic scene deblurring via meta-auxiliary learning
Zhixiang Chi, Yang Wang, Yuanhao Yu, and Jin Tang · 2021
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Design-bench: Benchmarks for data-driven offline model-based optimization
Brandon Trabucco, Xinyang Geng, Aviral Kumar, and Sergey Levine · 2022
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Bidirectional learning for offline infinite-width model-based optimization
Can Chen, Yingxue Zhang, Jie Fu, Xue Liu, and Mark Coates · 2022
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Data-driven model-based optimization via invariant representation learning
Han Qi, Yi Su, Aviral Kumar, and Sergey Levine · 2022
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Biological sequence design with gflownets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure FP Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, et al · 2022
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Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Robel: Robotics benchmarks for learning with low-cost robots
Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, and Vikash Kumar · 2020
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Model inversion networks for model-based optimization
Aviral Kumar and Sergey Levine · 2020
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Autofocused oracles for model-based design
Clara Fannjiang and Jennifer Listgarten · 2020
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Roma: Robust model adaptation for offline model-based optimization
Sihyun Yu, Sungsoo Ahn, Le Song, and Jinwoo Shin · 2021
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Can Chen, Xi Chen, Chen Ma, Zixuan Liu, and Xue Liu · 2022
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Unbiased implicit feedback via bi-level optimization
Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao Liu, and Xue Liu · 2022
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Metafscil: A meta-learning approach for few-shot class incremental learning
Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, and Jin Tang · 2022
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Bidirectional learning for offline model-based biological sequence design
Can Chen, Yingxue Zhang, Xue Liu, and Mark Coates · 2023
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Stochastic re-weighted gradient descent via distributionally robust optimization
Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj, and Arun Sai Suggala · 2023
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Bootstrapped training of score-conditioned generator for offline design of biological sequences
Minsu Kim, Federico Berto, Sungsoo Ahn, and Jinkyoo Park · 2023
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Parallel-mentoring for offline model-based optimization
Can Chen, Christopher Beckham, Zixuan Liu, Xue Liu, and Christopher Pal · 2023
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Structure-aware protein self-supervised learning
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Dataset distillation using parameter pruning
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