Yi-An Ma
Cited by
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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
Underspecification presents challenges for credibility in modern machine learning
A D’Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
Journal of Machine Learning Research, 2020
A complete recipe for stochastic gradient MCMC
YA Ma, T Chen, E Fox
Advances in Neural Information Processing Systems, 2917-2925, 2015
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
Efficient and scalable bayesian neural nets with rank-1 factors
M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ...
International conference on machine learning, 2782-2792, 2020
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
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
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
Deep mixture of experts via shallow embedding
X Wang, F Yu, L Dunlap, YA Ma, R Wang, A Mirhoseini, T Darrell, ...
Uncertainty in Artificial Intelligence, 552-562, 2020
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
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
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
On Approximate Thompson Sampling with Langevin Algorithms
E Mazumdar, A Pacchiano, YA Ma, M Jordan, P Bartlett
International Conference on Machine Learning, 6797-6807, 2020
Exploring a noisy van der Pol type oscillator with a stochastic approach
R Yuan, X Wang, Y Ma, B Yuan, P Ao
Physical Review E 87 (6), 062109, 2013
Irreversible samplers from jump and continuous Markov processes
YA Ma, EB Fox, T Chen, L Wu
Statistics and Computing 29 (1), 177-202, 2019
Dynamical behaviors determined by the Lyapunov function in competitive Lotka-Volterra systems
Y Tang, R Yuan, Y Ma
Physical Review E 87 (1), 012708, 2013
Lyapunov function as potential function: A dynamical equivalence
RS Yuan, YA Ma, B Yuan, P Ao
Chinese Physics B 23 (1), 010505, 2013
Potential function in dynamical systems and the relation with Lyapunov function
R Yuan, Y Ma, B Yuan, P Ao
Proceedings of the 30th Chinese Control Conference, 6573-6580, 2011
Variational refinement for importance sampling using the forward kullback-leibler divergence
G Jerfel, S Wang, C Wong-Fannjiang, KA Heller, Y Ma, MI Jordan
Uncertainty in Artificial Intelligence, 1819-1829, 2021
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
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