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ORCIDYi-An Ma
Yian Ma
Person information
- affiliation: University of California, Berkeley, CA, USA
- affiliation (former): University of Washington, WA, USA
- affiliation (former): Shanghai Jiao Tong University, China
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2020 – today
- 2024
[c25]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024: 235-243- [c24]Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS 2024: 415-423 - [c23]Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT 2024: 2438-2493 - [c22]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Reverse Diffusion Monte Carlo. ICLR 2024 - [c21]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. ICLR 2024 - [c20]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. ICML 2024 - [c19]Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. ICML 2024 - [c18]Ruijia Niu, Dongxia Wu, Kai Kim, Yian Ma, Duncan Watson-Parris, Rose Yu:
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. ICML 2024 - [c17]Sumanth Varambally, Yian Ma, Rose Yu:
Discovering Mixtures of Structural Causal Models from Time Series Data. ICML 2024 - [i39]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. CoRR abs/2401.06325 (2024) - [i38]Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. CoRR abs/2402.03726 (2024) - [i37]Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang:
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR abs/2402.18012 (2024) - [i36]Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris
, Rose Yu:
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. CoRR abs/2402.18846 (2024) - [i35]Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang:
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR abs/2405.16387 (2024) - [i34]Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR abs/2405.16734 (2024) - [i33]Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. CoRR abs/2406.00920 (2024) - [i32]Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf:
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. CoRR abs/2406.19049 (2024) - [i31]Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu:
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization. CoRR abs/2407.00610 (2024) - 2023
- [j7]Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang:
The Adaptive Spectral Koopman Method for Dynamical Systems. SIAM J. Appl. Dyn. Syst. 22(3): 1523-1551 (2023) - [c16]Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra:
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. ICML 2023: 15828-15860 - [c15]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. ICML 2023: 37624-37634 - [c14]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD 2023: 2559-2569 - [c13]Chaoyue Liu, Dmitriy Drusvyatskiy, Misha Belkin, Damek Davis, Yi-An Ma:
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. NeurIPS 2023 - [c12]Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner:
On the Convergence of Black-Box Variational Inference. NeurIPS 2023 - [c11]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. NeurIPS 2023 - [i30]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. CoRR abs/2305.04392 (2023) - [i29]Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner:
Black-Box Variational Inference Converges. CoRR abs/2305.15349 (2023) - [i28]Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma:
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR abs/2306.02601 (2023) - [i27]Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra:
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. CoRR abs/2306.08803 (2023) - [i26]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. CoRR abs/2307.02037 (2023) - [i25]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR abs/2307.14642 (2023) - [i24]Abhishek Roy, Geelon So, Yi-An Ma:
Optimization on Pareto sets: On a theory of multi-objective optimization. CoRR abs/2308.02145 (2023) - [i23]Sumanth Varambally, Yi-An Ma, Rose Yu:
Discovering Mixtures of Structural Causal Models from Time Series Data. CoRR abs/2310.06312 (2023) - [i22]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. CoRR abs/2310.14661 (2023) - [i21]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. CoRR abs/2310.18919 (2023) - 2022
- [j6]Yoav Freund, Yi-An Ma, Tong Zhang:
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. J. Mach. Learn. Res. 23: 214:1-214:32 (2022) - [j5]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23: 226:1-226:61 (2022) - [c10]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. KDD 2022: 2029-2038 - [i20]Ruoqi Shen, Liyao Gao, Yi-An Ma:
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. CoRR abs/2202.09885 (2022) - [i19]Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang:
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. CoRR abs/2206.01836 (2022) - [i18]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. CoRR abs/2206.04872 (2022) - [i17]Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang:
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection. CoRR abs/2207.11208 (2022) - 2021
- [j4]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. 22: 42:1-42:41 (2021) - [c9]Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD 2021: 1841-1851 - [c8]Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan:
Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI 2021: 1819-1829 - [i16]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. CoRR abs/2102.06684 (2021) - [i15]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR abs/2105.11982 (2021) - [i14]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR abs/2106.02770 (2021) - [i13]Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan:
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR abs/2106.15980 (2021) - [i12]Yoav Freund, Yi-An Ma, Tong Zhang:
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. CoRR abs/2110.01827 (2021) - [i11]Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin:
On Convergence of Federated Averaging Langevin Dynamics. CoRR abs/2112.05120 (2021) - 2020
- [c7]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [c6]Matthew D. Hoffman, Yian Ma:
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. ICML 2020: 4324-4341 - [c5]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [i10]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i9]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i8]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020)
2010 – 2019
- 2019
- [j3]Yi-An Ma, Emily B. Fox, Tianqi Chen, Lei Wu:
Irreversible samplers from jump and continuous Markov processes. Stat. Comput. 29(1): 177-202 (2019) - [j2]Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox:
Stochastic Gradient MCMC for State Space Models. SIAM J. Math. Data Sci. 1(3): 555-587 (2019) - [c4]Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez:
Deep Mixture of Experts via Shallow Embedding. UAI 2019: 552-562 - [i7]Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Is There an Analog of Nesterov Acceleration for MCMC? CoRR abs/1902.00996 (2019) - [i6]Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan:
Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR abs/1907.11826 (2019) - [i5]Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan:
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR abs/1908.10859 (2019) - 2018
- [c3]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML 2018: 763-772 - [i4]Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR abs/1802.05431 (2018) - [i3]Xin Wang, Fisher Yu, Ruth Wang, Yi-An Ma, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez:
Deep Mixture of Experts via Shallow Embedding. CoRR abs/1806.01531 (2018) - [i2]Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox:
Stochastic Gradient MCMC for State Space Models. CoRR abs/1810.09098 (2018) - [i1]Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan:
Sampling Can Be Faster Than Optimization. CoRR abs/1811.08413 (2018) - 2017
- [c2]Yi-An Ma, Nicholas J. Foti, Emily B. Fox:
Stochastic Gradient MCMC Methods for Hidden Markov Models. ICML 2017: 2265-2274 - 2015
- [c1]Yi-An Ma, Tianqi Chen, Emily B. Fox:
A Complete Recipe for Stochastic Gradient MCMC. NIPS 2015: 2917-2925 - 2014
- [j1]Yi-An Ma, Qijun Tan, Ruoshi Yuan, Bo Yuan, Ping Ao:
Potential Function in a Continuous Dissipative Chaotic System: Decomposition Scheme and Role of Strange Attractor. Int. J. Bifurc. Chaos 24(2) (2014)
Coauthor Index
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