Yi-An Ma

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 51

Top Topics

Diffusion Model

Reinforcement Learning

Delayed Feedback

Top Venues

J. Mach. Learn. Res.

Publications

Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR (2024)
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 (2024)
Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris, Rose Yu
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. CoRR (2024)
Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT (2024)
Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization. CoRR (2024)
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 (2024)
Kyurae Kim, Yi-An Ma, Jacob R. Gardner
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS (2024)
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)
Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner
Demystifying SGD with Doubly Stochastic Gradients. CoRR (2024)
Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR (2024)
Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. CoRR (2023)
Sumanth Varambally, Yi-An Ma, Rose Yu
Discovering Mixtures of Structural Causal Models from Time Series Data. CoRR (2023)
Chaoyue Liu, Dmitriy Drusvyatskiy, Misha Belkin, Damek Davis, Yi-An Ma
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. NeurIPS (2023)
Abhishek Roy, Geelon So, Yi-An Ma
Optimization on Pareto sets: On a theory of multi-objective optimization. CoRR (2023)
Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD (2023)
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 (2023)
Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR (2023)
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) (2023)
Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. ICML (2023)
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
Multi-fidelity Hierarchical Neural Processes. CoRR (2022)
Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. CoRR (2022)
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 (2022)
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 (2022)
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
Multi-fidelity Hierarchical Neural Processes. KDD (2022)
Ruoqi Shen, Liyao Gao, Yi-An Ma
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. CoRR (2022)
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 (2022)
Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin
On Convergence of Federated Averaging Langevin Dynamics. CoRR (2021)
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR (2021)
Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD (2021)
Ghassen Jerfel, Serena Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR (2021)
Yoav Freund, Yi-An Ma, Tong Zhang
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. CoRR (2021)
Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR (2021)
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 (2021)
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 (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
On Thompson Sampling with Langevin Algorithms. CoRR (2020)
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 (2020)
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)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
On Approximate Thompson Sampling with Langevin Algorithms. ICML (2020)
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) (2019)
Yi-An Ma, Emily B. Fox, Tianqi Chen, Lei Wu
Irreversible samplers from jump and continuous Markov processes. Stat. Comput. 29 (1) (2019)
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR (2019)
Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan
Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR (2019)
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)
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 (2019)
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
Sampling Can Be Faster Than Optimization. CoRR (2018)
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)
Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox
Stochastic Gradient MCMC for State Space Models. CoRR (2018)
Xin Wang, Fisher Yu, Ruth Wang, Yi-An Ma, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez
Deep Mixture of Experts via Shallow Embedding. CoRR (2018)
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 (2018)
Yi-An Ma, Nicholas J. Foti, Emily B. Fox
Stochastic Gradient MCMC Methods for Hidden Markov Models. ICML (2017)
Yi-An Ma, Tianqi Chen, Emily B. Fox
A Complete Recipe for Stochastic Gradient MCMC. NIPS (2015)
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)