Yi-An Ma

According to our database, Yi-An Ma authored at least 56 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

ClimaQA: An Automated Evaluation Framework for Climate Foundation Models.

Veeramakali Vignesh Manivannan, Yasaman Jafari, Srikar Eranky, Spencer Ho, Rose Yu, Duncan Watson-Parris, Yian Ma, Leon Bergen, Taylor Berg-Kirkpatrick

CoRR, 2024

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs.

Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma

CoRR, 2024

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery.

Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yi-An Ma, Biwei Huang

CoRR, 2024

Log-concave Sampling over a Convex Body with a Barrier: a Robust and Unified Dikin Walk.

Yuzhou Gu, Nikki Lijing Kuang, Yi-An Ma, Zhao Song, Lichen Zhang

CoRR, 2024

Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization.

Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu

CoRR, 2024

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.

Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf

CoRR, 2024

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference.

Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang

CoRR, 2024

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints.

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

CoRR, 2024

Discovering Mixtures of Structural Causal Models from Time Series Data.

Sumanth Varambally, Yian Ma, Rose Yu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling.

Ruijia Niu, Dongxia Wu, Kai Kim, Yian Ma, Duncan Watson-Parris, Rose Yu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Demystifying SGD with Doubly Stochastic Gradients.

Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Faster Sampling via Stochastic Gradient Proximal Sampler.

Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy.

Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Reverse Diffusion Monte Carlo.

Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo.

Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes.

Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

Kyurae Kim, Yi-An Ma, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

The Adaptive Spectral Koopman Method for Dynamical Systems.

Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang

SIAM J. Appl. Dyn. Syst., September, 2023

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy.

Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg

CoRR, 2023

Optimization on Pareto sets: On a theory of multi-objective optimization.

Abhishek Roy, Geelon So, Yi-An Ma

CoRR, 2023

Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach.

Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang

CoRR, 2023

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems.

Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma

CoRR, 2023

Black-Box Variational Inference Converges.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner

CoRR, 2023

Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation.

Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Convergence of Black-Box Variational Inference.

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems.

Chaoyue Liu, Dmitriy Drusvyatskiy, Misha Belkin, Damek Davis, Yi-An Ma

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Bayesian Active Learning for Accelerating Stochastic Simulation.

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Disentangled Multi-Fidelity Deep Bayesian Active Learning.

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu

Proceedings of the International Conference on Machine Learning, 2023

Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning.

Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra

Proceedings of the International Conference on Machine Learning, 2023

2022

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint.

Yoav Freund, Yi-An Ma, Tong Zhang

J. Mach. Learn. Res., 2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

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

J. Mach. Learn. Res., 2022

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection.

Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang

CoRR, 2022

Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization.

Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang

CoRR, 2022

On Optimal Early Stopping: Over-informative versus Under-informative Parametrization.

Ruoqi Shen, Liyao Gao, Yi-An Ma

CoRR, 2022

Multi-fidelity Hierarchical Neural Processes.

Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm.

Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

J. Mach. Learn. Res., 2021

On Convergence of Federated Averaging Langevin Dynamics.

Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin

CoRR, 2021

Accelerating Stochastic Simulation with Interactive Neural Processes.

Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

CoRR, 2021

DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting.

Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

CoRR, 2021

Variational refinement for importance sampling using the forward Kullback-Leibler divergence.

Ghassen Jerfel, Serena Lutong Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Quantifying Uncertainty in Deep Spatiotemporal Forecasting.

Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

On Thompson Sampling with Langevin Algorithms.

Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan

CoRR, 2020

On Approximate Thompson Sampling with Langevin Algorithms.

Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett

Proceedings of the 37th International Conference on Machine Learning, 2020

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics.

Matthew D. Hoffman, Yian Ma

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.

Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Stochastic Gradient MCMC for State Space Models.

Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox

SIAM J. Math. Data Sci., 2019

Irreversible samplers from jump and continuous Markov processes.

Yi-An Ma, Emily B. Fox, Tianqi Chen, Lei Wu

Stat. Comput., 2019

Bayesian Robustness: A Nonasymptotic Viewpoint.

Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan

CoRR, 2019

Is There an Analog of Nesterov Acceleration for MCMC?

Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan

CoRR, 2019

Deep Mixture of Experts via Shallow Embedding.

Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Sampling Can Be Faster Than Optimization.

Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan

CoRR, 2018

Deep Mixture of Experts via Shallow Embedding.

Xin Wang, Fisher Yu, Ruth Wang, Yi-An Ma, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez

CoRR, 2018

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo.

Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Stochastic Gradient MCMC Methods for Hidden Markov Models.

Yi-An Ma, Nicholas J. Foti, Emily B. Fox

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

A Complete Recipe for Stochastic Gradient MCMC.

Yi-An Ma, Tianqi Chen, Emily B. Fox

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Potential Function in a Continuous Dissipative Chaotic System: Decomposition Scheme and Role of Strange Attractor.

Yi-An Ma, Qijun Tan, Ruoshi Yuan, Bo Yuan, Ping Ao

Int. J. Bifurc. Chaos, 2014