受强制性开放获取政策约束的文章 - Yi-An Ma了解详情
可在其他位置公开访问的文章：21 篇
Single-cell mRNA quantification and differential analysis with Census
X Qiu, A Hill, J Packer, D Lin, YA Ma, C Trapnell
Nature methods 14 (3), 309-315, 2017
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Mapping transcriptomic vector fields of single cells
X Qiu, Y Zhang, JD Martin-Rufino, C Weng, S Hosseinzadeh, D Yang, ...
Cell 185 (4), 690-711. e45, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense, US National …
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ...
Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy, Bill & Melinda …
Sampling can be faster than optimization
YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan
Proceedings of the National Academy of Sciences 116 (42), 20881-20885, 2019
强制性开放获取政策： US Department of Defense
Is there an analog of Nesterov acceleration for gradient-based MCMC?
YA Ma, NS Chatterji, X Cheng, N Flammarion, PL Bartlett, MI Jordan
Bernoulli 27 (3), 1942-1992, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
On the theory of variance reduction for stochastic gradient Monte Carlo
N Chatterji, N Flammarion, YA Ma, P Bartlett, M Jordan
International Conference on Machine Learning, 764-773, 2018
强制性开放获取政策： US National Science Foundation, US Department of Defense
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
W Mou, YA Ma, MJ Wainwright, PL Bartlett, MI Jordan
Journal of Machine Learning Research 22 (42), 1-41, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
D Wu, L Gao, X Xiong, M Chinazzi, A Vespignani, YA Ma, R Yu
27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 1841–1851, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense, US National …
Irreversible samplers from jump and continuous Markov processes
YA Ma, EB Fox, T Chen, L Wu
Statistics and Computing 29 (1), 177-202, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Stochastic Gradient MCMC for State Space Models
C Aicher, YA Ma, NJ Foti, EB Fox
SIAM Journal on Mathematics of Data Science 1 (3), 555-587, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Stochastic gradient MCMC methods for hidden Markov models
YA Ma, NJ Foti, EB Fox
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
强制性开放获取政策： US National Science Foundation, US Department of Defense
Disentangled multi-fidelity deep bayesian active learning.
D Wu, R Niu, M Chinazzi, Y Ma, R Yu
In International Conference on Machine Learning, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
Reverse Diffusion Monte Carlo
X Huang, H Dong, HAO Yifan, Y Ma, T Zhang
The Twelfth International Conference on Learning Representations, 2024
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
R Shen, L Gao, Y Ma
arXiv preprint arXiv:2202.09885, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy
When is the convergence time of Langevin algorithms dimension independent? A composite optimization viewpoint
Y Freund, YA Ma, T Zhang
The Journal of Machine Learning Research 23 (1), 9604-9635, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy
Multi-fidelity hierarchical neural processes
D Wu, M Chinazzi, A Vespignani, YA Ma, R Yu
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
Deep bayesian active learning for accelerating stochastic simulation
D Wu, R Niu, M Chinazzi, A Vespignani, YA Ma, R Yu
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
Posterior sampling with delayed feedback for reinforcement learning with linear function approximation
NL Kuang, M Yin, M Wang, YX Wang, Y Ma
Advances in Neural Information Processing Systems 36, 6782-6824, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
The adaptive spectral koopman method for dynamical systems
B Li, Y Ma, JN Kutz, X Yang
SIAM Journal on Applied Dynamical Systems 22 (3), 1523-1551, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
A Karbasi, NL Kuang, Y Ma, S Mitra
International Conference on Machine Learning, 15828-15860, 2023
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
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