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Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules.
Bootstrap methods: another look at the jackknife
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ZINC- a free database of commercially available compounds for virtual screening
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Gaussian process optimization in the bandit setting: No regret and experimental design
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Denoising diffusion implicit models
Song, J., C. Meng, and S. Ermon (2020) · 2010
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Auto-encoding variational bayes
Kingma, D. P. and M. Welling (2013) · 2013
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Finite-time analysis of kernelised contextual bandits
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Adam: A method for stochastic optimization
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
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Training deep nets with sublinear memory cost
Chen, T., B. Xu, C. Zhang, and C. Guestrin (2016) · 2016
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Memory-efficient backpropagation through time
Gruslys, A., R. Munos, I. Danihelka, M. Lanctot, and A. Graves (2016) · 2016
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Deep exploration via bootstrapped DQN
Osband, I., C. Blundell, A. Pritzel, and B. Van Roy (2016) · 2016
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Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., D. A. Bolotin, M. V. Meer, D. R. Usmanova, A. S. Mishin, G. V. Sharonov, D. N. Ivankov, N. G. Bozhanova, M. S. Baranov, O. Soylemez, et al. (2016) · 2016
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A unified view of entropy-regularized markov decision processes
Neu, G., A. Jonsson, and V. Gómez (2017) · 2017
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Proximal policy optimization algorithms
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov (2017) · 2017
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., R. Calandra, R. McAllister, and S. Levine (2018) · 2018
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A tutorial on bayesian optimization
Frazier, P. I. (2018) · 2018
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Garnelo, M., J. Schwarz, D. Rosenbaum, F. Viola, D. J. Rezende, S. Eslami, and Y. W. Teh (2018) · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik (2018) · 2018
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Reinforcement learning: Theory and algorithms
Agarwal, A., N. Jiang, S. M. Kakade, and W. Sun (2019) · 2019
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Batched multi-armed bandits problem
Gao, Z., Y. Han, Z. Ren, and Z. Zhou (2019) · 2019
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A theory of regularized markov decision processes
Geist, M., B. Scherrer, and O. Pietquin (2019) · 2019
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Garbage in, reward out: Bootstrapping exploration in multi-armed bandits
Kveton, B., C. Szepesvari, S. Vaswani, Z. Wen, T. Lattimore, and M. Ghavamzadeh (2019) · 2019
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BoTorch: A framework for efficient monte-carlo bayesian optimization
Balandat, M., B. Karrer, D. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy (2020) · 2020
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Denoising diffusion probabilistic models
Ho, J., A. Jain, and P. Abbeel (2020) · 2020
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Contextual bandits with continuous actions: Smoothing, zooming, and adapting
Krishnamurthy, A., J. Langford, A. Slivkins, and C. Zhang (2020) · 2020
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Bandit algorithms
Lattimore, T. and C. Szepesvári (2020) · 2020
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Neural contextual bandits with ucb-based exploration
Zhou, D., L. Li, and Q. Gu (2020) · 2020
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Proceedings of the first workshop on interactive learning for natural language processing
Brantley, K., S. Dan, I. Gurevych, J.-U. Lee, F. Radlinski, H. Schütze, E. Simpson, and L. Yu (2021) · 2021
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Mitigating covariate shift in imitation learning via offline data without great coverage
Chang, J. D., M. Uehara, D. Sreenivas, R. Kidambi, and W. Sun (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and A. Nichol (2021) · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen (2021) · 2021
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Directly fine-tuning diffusion models on differentiable rewards
Clark, K., P. Vicol, K. Swersky, and D. J. Fleet (2023) · 2023
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Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., W. Xiong, D. Goyal, R. Pan, S. Diao, J. Zhang, K. Shum, and T. Zhang (2023) · 2023
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DPOK: Reinforcement learning for fine-tuning text-to-image diffusion models
Fan, Y., O. Watkins, Y. Du, H. Liu, M. Ryu, C. Boutilier, P. Abbeel, M. Ghavamzadeh, K. Lee, and K. Lee (2023) · 2023
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Generative flow networks assisted biological sequence editing
Ghari, P. M., A. Tseng, G. Eraslan, R. Lopez, T. Biancalani, G. Scalia, and E. Hajiramezanali (2023) · 2023
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Improving black-box optimization in VAE latent space using decoder uncertainty
Notin, P., J. M. Hernández-Lobato, and Y. Gal (2021) · 2021
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Learning transferable visual models from natural language supervision
Radford, A., J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever (2021) · 2021
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Diffusion models as plug-and-play priors
Graikos, A., N. Malkin, N. Jojic, and D. Samaras (2022) · 2022
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Equivariant 3d-conditional diffusion models for molecular linker design
Igashov, I., H. Stärk, C. Vignac, V. G. Satorras, P. Frossard, M. Welling, M. Bronstein, and B. Correia (2022) · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., G. Corso, J. Chang, R. Barzilay, and T. Jaakkola (2022) · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., S. Lee, and S. J. Hwang (2022) · 2022
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Let us build bridges: Understanding and extending diffusion generative models
Liu, X., L. Wu, M. Ye, and Q. Liu (2022) · 2022
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Gruver, N., S. Stanton, N. C. Frey, T. G. Rudner, I. Hotzel, J. Lafrance-Vanasse, A. Rajpal, K. Cho, and A. G. Wilson (2023) · 2023
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Diffusion models for black-box optimization
Krishnamoorthy, S., S. M. Mashkaria, and A. Grover (2023) · 2023
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Aligning text-to-image models using human feedback
Lee, K., H. Liu, M. Ryu, O. Watkins, Y. Du, C. Boutilier, P. Abbeel, M. Ghavamzadeh, and S. S. Gu (2023) · 2023
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Latent diffusion model for dna sequence generation
Li, Z., Y. Ni, T. A. B. Huygelen, A. Das, G. Xia, G.-B. Stan, and Y. Zhao (2023) · 2023
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Flow matching for generative modeling
Lipman, Y., R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le (2023) · 2023
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I2 sb: Image-to-image schr \ \backslash ” odinger bridge
Liu, G.-H., A. Vahdat, D.-A. Huang, E. A. Theodorou, W. Nie, and A. Anandkumar (2023) · 2023
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Aligning text-to-image diffusion models with reward backpropagation
Prabhudesai, M., A. Goyal, D. Pathak, and K. Fragkiadaki (2023) · 2023
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Diffusion schr \ \backslash ” odinger bridge matching
Shi, Y., V. De Bortoli, A. Campbell, and A. Doucet (2023) · 2023
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Aligned diffusion schr \ \backslash ” odinger bridges
Somnath, V. R., M. Pariset, Y.-P. Hsieh, M. R. Martinez, A. Krause, and C. Bunne (2023) · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
Tong, A., N. Malkin, G. Huguet, Y. Zhang, J. Rector-Brooks, K. Fatras, G. Wolf, and Y. Bengio (2023) · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al. (2023) · 2023
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GraphGUIDE: interpretable and controllable conditional graph generation with discrete bernoulli diffusion
Tseng, A. M., N. L. Diamant, T. Biancalani, and G. Scalia (2023) · 2023
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Digress: Discrete denoising diffusion for graph generation
Vignac, C., I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard (2023) · 2023
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Diffusion model alignment using direct preference optimization
Wallace, B., M. Dang, R. Rafailov, L. Zhou, A. Lou, S. Purushwalkam, S. Ermon, C. Xiong, S. Joty, and N. Naik (2023) · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al. (2023) · 2023
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Better aligning text-to-image models with human preference
Wu, X., K. Sun, F. Zhu, R. Zhao, and H. Li (2023) · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., X. Liu, Y. Wu, Y. Tong, Q. Li, M. Ding, J. Tang, and Y. Dong (2023) · 2023
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Using human feedback to fine-tune diffusion models without any reward model
Yang, K., J. Tao, J. Lyu, C. Ge, J. Chen, Q. Li, W. Shen, X. Zhu, and X. Li (2023) · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Yuan, H., K. Huang, C. Ni, M. Chen, and M. Wang (2023) · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn (2024) · 2024
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Fine-tuning of continuous-time diffusion models as entropy-regularized control
Uehara, M., Y. Zhao, K. Black, E. Hajiramezanali, G. Scalia, N. L. Diamant, A. M. Tseng, T. Biancalani, and S. Levine (2024) · 2024
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Pre-training protein encoder via siamese sequence-structure diffusion trajectory prediction
Zhang, Z., M. Xu, A. C. Lozano, V. Chenthamarakshan, P. Das, and J. Tang (2024) · 2024
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