Yi-An Ma

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ruoshi YuanDepartment of Bioengineering at the University of California, BerkeleyVerified email at berkeley.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Xiaojie QiuWhitehead Institute, MITVerified email at wi.mit.edu
Ping AoProfessor of Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, ShanghaiVerified email at sjtu.edu.cn
Tianqi ChenCarnegie Mellon UniversityVerified email at cmu.edu
Nicolas FlammarionEPFLVerified email at epfl.ch
Niladri S. ChatterjiPostdoctoral Researcher, Department of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Hong QianOlga Jung Wan Endowed Professor of Applied Mathematics, University of WashingtonVerified email at u.washington.edu
Ghassen JerfelResearch @Waymo. Ex-Google Brain, UC Berkeley, Duke, Princeton.Verified email at alumni.princeton.edu
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Matteo ChinazziResearch Associate Professor, Northeastern UniversityVerified email at northeastern.edu
Rose YuAssistant Professor, University of California, San DiegoVerified email at ucsd.edu
Alessandro VespignaniSternberg Family Distinguished Professor, Northeastern University, BostonVerified email at neu.edu
Dustin TranResearch Scientist, GoogleVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Balaji LakshminarayananSenior Staff Research Scientist at Google BrainVerified email at google.com
Wenlong MouUC BerkeleyVerified email at eecs.berkeley.edu
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyVerified email at berkeley.edu

Yi-An Ma

UC San Diego

Verified email at ucsd.edu - Homepage

scalable machine learning Bayesian inference optimization stochastic processes dynamical systems


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	928	2017
A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in Neural Information Processing Systems, 2917-2925, 2015	437	2015
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	395	2020
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	136	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	129	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	121	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	103	2021
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	86	2018
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	71	2022
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	55	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	54	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	44	2013
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	39	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	34*	2019
Lyapunov function as potential function: A dynamical equivalence RS Yuan, YA Ma, B Yuan, P Ao Chinese Physics B 23 (1), 010505, 2013	31	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	29*	2011
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	23*	2020
Potential function in a continuous dissipative chaotic system: Decomposition scheme and role of strange attractor Y Ma, Q Tan, R Yuan, B Yuan, P Ao International Journal of Bifurcation and Chaos 24 (02), 1450015, 2014	23	2014
Black-Box Variational Inference as Distilled Langevin Dynamics M Hoffman, YA Ma International Conference on Machine Learning 37, 2020	22*	2020
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	21	2017

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