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
Rose YuAssociate Professor, University of California, San DiegoVerified email at ucsd.edu
Xiaojie QiuAssistant Professor, BASE, Department of Genetics & Computer Science, StanfordVerified email at stanford.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ping AoProfessor of Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, ShanghaiVerified email at sjtu.edu.cn
Matteo ChinazziResearch Associate Professor, Northeastern UniversityVerified email at northeastern.edu
Tianqi ChenCarnegie Mellon UniversityVerified email at cmu.edu
Nicolas FlammarionEPFLVerified email at epfl.ch
Alessandro VespignaniSternberg Family Distinguished Professor, Northeastern University, BostonVerified email at neu.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
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
Dustin TranResearch Scientist, GoogleVerified email at google.com
Jasper SnoekGoogle BrainVerified email at google.com
Balaji LakshminarayananSenior Staff Research Scientist at Google DeepMindVerified email at google.com
Yuansi ChenSeminar for Statistics, D-MATH, ETH ZurichVerified email at stat.math.ethz.ch
Chi JinAssistant Professor, Princeton UniversityVerified email at princeton.edu

Yi-An Ma

Other namesYian 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	1525	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	836	2020
A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in Neural Information Processing Systems, 2917-2925, 2015	605	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	338	2022
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	249	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	247	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	236	2019
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	178	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	124	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	108	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	96	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	68	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	57*	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	49	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	48	2013
Lyapunov function as potential function: A dynamical equivalence RS Yuan, YA Ma, B Yuan, P Ao Chinese Physics B 23 (1), 010505, 2013	44	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	42*	2019
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	41	2021
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	36*	2011
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	35	2019

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