Yi-An Ma 个人学术档案 - 学术资源搜索

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引用	4520	4134
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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Ruoshi YuanDepartment of Bioengineering at the University of California, Berkeley在 berkeley.edu 的电子邮件经过验证
Emily B. FoxStanford University在 stanford.edu 的电子邮件经过验证
Peter BartlettProfessor, EECS and Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Ping AoProfessor of Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai在 sjtu.edu.cn 的电子邮件经过验证
Tianqi ChenCarnegie Mellon University在 cmu.edu 的电子邮件经过验证
Rose YuAssociate Professor, University of California, San Diego在 ucsd.edu 的电子邮件经过验证
Nicolas FlammarionEPFL在 epfl.ch 的电子邮件经过验证
Matteo ChinazziResearch Associate Professor, Northeastern University在 northeastern.edu 的电子邮件经过验证
Alessandro VespignaniSternberg Family Distinguished Professor, Northeastern University, Boston在 neu.edu 的电子邮件经过验证
Hong QianOlga Jung Wan Endowed Professor of Applied Mathematics, University of Washington在 u.washington.edu 的电子邮件经过验证
Ghassen JerfelResearch @Waymo. Ex-Google Brain, UC Berkeley, Duke, Princeton.在 alumni.princeton.edu 的电子邮件经过验证
Katherine HellerGoogle Research在 gatsby.ucl.ac.uk 的电子邮件经过验证
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford University在 cs.stanford.edu 的电子邮件经过验证
Dustin TranResearch Scientist, Google在 google.com 的电子邮件经过验证
Jasper SnoekGoogle Brain在 google.com 的电子邮件经过验证
Balaji LakshminarayananSenior Staff Research Scientist at Google DeepMind在 google.com 的电子邮件经过验证
Yuansi ChenDepartment of Statistical Science, Duke University在 duke.edu 的电子邮件经过验证
Chi JinAssistant Professor, Princeton University在 princeton.edu 的电子邮件经过验证
Xiang ChengMassachusetts Institute of Technology在 berkeley.edu 的电子邮件经过验证

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Yi-An Ma

其他姓名Yian Ma

UC San Diego

在 ucsd.edu 的电子邮件经过验证 - 首页

scalable machine learning Bayesian inference optimization stochastic processes dynamical systems


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	1316	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	718	2020
A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in Neural Information Processing Systems, 2917-2925, 2015	561	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	237*	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	222	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	202	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	189	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	154	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	102	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	97	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	85	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	56	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	46*	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	46	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	40*	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	40	2013
Lyapunov function as potential function: A dynamical equivalence RS Yuan, YA Ma, B Yuan, P Ao Chinese Physics B 23 (1), 010505, 2013	36	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	34*	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	32	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	30	2017

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