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| A Generalist Agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 815 | 2022 |
| Super-samples from kernel herding Y Chen, M Welling, A Smola The Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial …, 2010 | 389 | 2010 |
| Learning to Learn without Gradient Descent by Gradient Descent Y Chen, MW Hoffman, SG Colmenarejo, M Denil, TP Lillicrap, M Botvinick, ... Thirty-fourth International Conference on Machine Learning, 2017 | 341* | 2017 |
| Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget K Anoop, Y Chen, M Welling Proceedings of the 31th International Conference on Machine Learning, (ICML …, 2014 | 293* | 2014 |
| Parallel multiscale autoregressive density estimation S Reed, A Oord, N Kalchbrenner, SG Colmenarejo, Z Wang, Y Chen, ... International Conference on Machine Learning, 2912-2921, 2017 | 238 | 2017 |
| Variational Gaussian Process State-Space Models R Frigola, Y Chen, C Rasmussen Advances in Neural Information Processing Systems, 3680--3688, 2014 | 228 | 2014 |
| Sample Efficient Adaptive Text-to-Speech Y Chen, Y Assael, B Shillingford, D Budden, S Reed, H Zen, Q Wang, ... International Conference on Learning Representations, 2019 | 171 | 2019 |
| Bayesian Optimization in AlphaGo Y Chen, A Huang, Z Wang, I Antonoglou, J Schrittwieser, D Silver, ... arXiv preprint arXiv:1812.06855, 2018 | 157 | 2018 |
| Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions S Reed, Y Chen, T Paine, A Oord, SM Eslami, D Rezende, O Vinyals, ... arXiv preprint arXiv:1710.10304, 2017 | 93 | 2017 |
| Benchmarks for Deep Off-Policy Evaluation J Fu, M Norouzi, O Nachum, G Tucker, Z Wang, A Novikov, M Yang, ... International Conference on Learning Representations, 2021 | 83 | 2021 |
| Learning Deep Features in Instrumental Variable Regression L Xu, Y Chen, S Srinivasan, N de Freitas, A Doucet, A Gretton International Conference on Learning Representations, 2021 | 71 | 2021 |
| Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data Y Gal, Y Chen, Z Ghahramani Proceedings of The 32nd International Conference on Machine Learning, 645–654, 2015 | 57 | 2015 |
| Probabilistic programming with programmable inference VK Mansinghka, U Schaechtle, S Handa, A Radul, Y Chen, M Rinard Proceedings of the 39th ACM SIGPLAN Conference on Programming Language …, 2018 | 56 | 2018 |
| Shaking the foundations: delusions in sequence models for interaction and control PA Ortega, M Kunesch, G Delétang, T Genewein, J Grau-Moya, J Veness, ... arXiv preprint arXiv:2110.10819, 2021 | 54 | 2021 |
| Distributed Inference for Dirichlet Process Mixture Models H Ge, Y Chen, M Wan, Z Ghahramani Proceedings of The 32nd International Conference on Machine Learning, 2276-2284, 2015 | 46 | 2015 |
| Towards learning universal hyperparameter optimizers with transformers Y Chen, X Song, C Lee, Z Wang, R Zhang, D Dohan, K Kawakami, ... Advances in Neural Information Processing Systems 35, 32053-32068, 2022 | 45 | 2022 |
| Modular meta-learning with shrinkage Y Chen, AL Friesen, F Behbahani, A Doucet, D Budden, M Hoffman, ... Advances in Neural Information Processing Systems 33, 2020 | 45 | 2020 |
| Perturbations, Optimization, and Statistics AL Yuille, R Adams, RS Zemel, Y Chen, M Welling, A Gane, T Jaakkola, ... MIT Press, 2017 | 45* | 2017 |
| Discovering evolution strategies via meta-black-box optimization R Lange, T Schaul, Y Chen, T Zahavy, V Dalibard, C Lu, S Singh, ... Proceedings of the Companion Conference on Genetic and Evolutionary …, 2023 | 41 | 2023 |
Nando de FreitasCIFAR & DeepMindZweryfikowany adres z google.com
