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)

A Complete Recipe for Stochastic Gradient MCMC. - Yi-An Ma; Tianqi Chen 001; Emily B. Fox (NIPS, 2015)

Stochastic Gradient MCMC Methods for Hidden Markov Models. - Yi-An Ma; Nicholas J. Foti; Emily B. Fox (ICML, 2017)

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 (ICML, 2018)

Deep Mixture of Experts via Shallow Embedding. - Xin Wang 066; Fisher Yu 001; Ruth Wang; Yi-An Ma; Azalia Mirhoseini; Trevor Darrell; Joseph E. Gonzalez (CoRR, 2018)

Sampling Can Be Faster Than Optimization. - Yi-An Ma; Yuansi Chen; Chi Jin 001; Nicolas Flammarion; Michael I. Jordan (CoRR, 2018)

Deep Mixture of Experts via Shallow Embedding. - Xin Wang 066; Fisher Yu 001; Lisa Dunlap; Yi-An Ma; Ruth Wang; Azalia Mirhoseini; Trevor Darrell; Joseph E. Gonzalez (UAI, 2019)

Is There an Analog of Nesterov Acceleration for MCMC? - Yi-An Ma; Niladri S. Chatterji; Xiang Cheng 006; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Bayesian Robustness: A Nonasymptotic Viewpoint. - Kush Bhatia; Yi-An Ma; Anca D. Dragan; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Irreversible samplers from jump and continuous Markov processes. - Yi-An Ma; Emily B. Fox; Tianqi Chen 001; Lei Wu (Stat. Comput., 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)

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 (ICML, 2020)

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. - Matthew D. Hoffman; Yian Ma (ICML, 2020)

On Approximate Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Michael I. Jordan; Peter L. Bartlett (ICML, 2020)

On Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (CoRR, 2020)

Quantifying Uncertainty in Deep Spatiotemporal Forecasting. - Dongxia Wu; Liyao Gao; Matteo Chinazzi; Xinyue Xiong; Alessandro Vespignani; Yi-An Ma; Rose Yu (KDD, 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 (UAI, 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)

Accelerating Stochastic Simulation with Interactive Neural Processes. - Dongxia Wu; Matteo Chinazzi; Alessandro Vespignani; Yi-An Ma; Rose Yu (CoRR, 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)

Multi-fidelity Hierarchical Neural Processes. - Dongxia Wu; Matteo Chinazzi; Alessandro Vespignani; Yi-An Ma; Rose Yu (KDD, 2022)

On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. - Ruoqi Shen; Liyao Gao; Yi-An Ma (CoRR, 2022)

Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. - Yi-An Ma; Teodor Vanislavov Marinov; Tong Zhang 001 (CoRR, 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)

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; et al. (J. Mach. Learn. Res., 2022)

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. - Yoav Freund; Yi-An Ma; Tong Zhang 001 (J. Mach. Learn. Res., 2022)

Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. - Amin Karbasi; Nikki Lijing Kuang; Yi-An Ma; Siddharth Mitra (ICML, 2023)

Disentangled Multi-Fidelity Deep Bayesian Active Learning. - Dongxia Wu; Ruijia Niu; Matteo Chinazzi; Yi-An Ma; Rose Yu (ICML, 2023)

Deep Bayesian Active Learning for Accelerating Stochastic Simulation. - Dongxia Wu; Ruijia Niu; Matteo Chinazzi; Alessandro Vespignani; Yi-An Ma; Rose Yu (KDD, 2023)

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. - Chaoyue Liu 001; Dmitriy Drusvyatskiy; Mikhail Belkin; Damek Davis; Yi-An Ma (NeurIPS, 2023)

On the Convergence of Black-Box Variational Inference. - Kyurae Kim; Jisu Oh; Kaiwen Wu; Yi-An Ma; Jacob R. Gardner (NeurIPS, 2023)

Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. - Nikki Lijing Kuang; Ming Yin 003; Mengdi Wang; Yu-Xiang Wang 003; Yian Ma (NeurIPS, 2023)

Black-Box Variational Inference Converges. - Kyurae Kim; Kaiwen Wu; Jisu Oh; Yi-An Ma; Jacob R. Gardner (CoRR, 2023)

Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. - Xunpeng Huang; Hanze Dong; Yifan Hao 002; Yian Ma; Tong Zhang 001 (CoRR, 2023)

Optimization on Pareto sets: On a theory of multi-objective optimization. - Abhishek Roy; Geelon So; Yi-An Ma (CoRR, 2023)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg (CoRR, 2023)

The Adaptive Spectral Koopman Method for Dynamical Systems. - Bian Li; Yi-An Ma; J. Nathan Kutz; Xiu Yang (SIAM J. Appl. Dyn. Syst., 2023)

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? - Kyurae Kim; Yi-An Ma; Jacob R. Gardner (AISTATS, 2024)

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. - Dongxia Wu; Tsuyoshi Idé; Georgios Kollias; Jirí Navrátil 001; Aurélie C. Lozano; Naoki Abe; Yi-An Ma; Rose Yu (AISTATS, 2024)

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi-An Ma; Tong Zhang 001 (COLT, 2024)

Reverse Diffusion Monte Carlo. - Xunpeng Huang; Hanze Dong; Yifan Hao 002; Yian Ma; Tong Zhang 001 (ICLR, 2024)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg; Zhiqi Bu (ICLR, 2024)

Faster Sampling via Stochastic Gradient Proximal Sampler. - Xunpeng Huang; Difan Zou; Hanze Dong; Yian Ma; Tong Zhang 001 (ICML, 2024)

Demystifying SGD with Doubly Stochastic Gradients. - Kyurae Kim; Joohwan Ko; Yian Ma; Jacob R. Gardner (ICML, 2024)

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. - Ruijia Niu; Dongxia Wu; Kai Kim; Yian Ma; Duncan Watson-Parris; Rose Yu (ICML, 2024)

Discovering Mixtures of Structural Causal Models from Time Series Data. - Sumanth Varambally; Yian Ma; Rose Yu (ICML, 2024)

Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk. - Yuzhou Gu; Nikki Lijing Kuang; Yian Ma; Zhao Song 002; Lichen Zhang 003 (NeurIPS, 2024)

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi Zhang; Yian Ma; Tong Zhang 001 (NeurIPS, 2024)

On Convergence of Federated Averaging Langevin Dynamics. - Wei Deng 002; Qian Zhang 067; Yian Ma; Zhao Song 002; Guang Lin 001 (UAI, 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)

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 002; Lichen Zhang 003 (CoRR, 2024)

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs. - Ruijia Niu; Dongxia Wu; Rose Yu; Yi-An Ma (CoRR, 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)

Discovering Latent Structural Causal Models from Spatio-Temporal Data. - Kun Wang; Sumanth Varambally; Duncan Watson-Parris; Yi-An Ma; Rose Yu (CoRR, 2024)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. - Lingkai Kong; Yuanqi Du; Wenhao Mu; Kirill Neklyudov; Valentin De Bortoli; Dongxia Wu; Haorui Wang; Aaron M. Ferber; Yian Ma; Carla P. Gomes; Chao Zhang 014 (AISTATS, 2025)

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 (AISTATS, 2025)

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. - Yingyu Lin; Yuxing Huang; Wenqin Liu; Haoran Deng; Ignavier Ng; Kun Zhang 001; Mingming Gong; Yian Ma; Biwei Huang (ICLR, 2025)

ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models. - Veeramakali Vignesh Manivannan; Yasaman Jafari; Srikar Eranky; Spencer Ho; Rose Yu; Duncan Watson-Parris; Yian Ma; Leon Bergen; Taylor Berg-Kirkpatrick (ICLR, 2025)

Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action. - Jasmine Chiat Ling Ong; Yilin Ning; Mingxuan Liu; Yian Ma; Zhao Liang; Kuldev Singh; Robert T. Chang; Silke Vogel; John CW Lim; Iris Siu Kwan Tan; Oscar Freyer; Stephen Gilbert; et al. (CoRR, 2025)

Learning to Steer Learners in Games. - Yizhou Zhang; Yi-An Ma; Eric Mazumdar (CoRR, 2025)

Purifying Approximate Differential Privacy with Randomized Post-processing. - Yingyu Lin; Erchi Wang; Yi-An Ma; Yu-Xiang Wang 003 (CoRR, 2025)

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness. - Yilin Ning; Yian Ma; Mingxuan Liu; Xin Li 079; Nan Liu 003 (CoRR, 2025)

Capturing Conditional Dependence via Auto-regressive Diffusion Models. - Xunpeng Huang; Yujin Han; Difan Zou; Yian Ma; Tong Zhang 001 (CoRR, 2025)

Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees. - Nishant Jain; Xunpeng Huang; Yian Ma; Tong Zhang 001 (CoRR, 2025)

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. - Kyurae Kim; Yi-An Ma; Trevor Campbell; Jacob R. Gardner (CoRR, 2025)

Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion. - Xunpeng Huang; Yingyu Lin; Nikki Lijing Kuang; Hanze Dong; Difan Zou; Yian Ma; Tong Zhang 001 (CoRR, 2025)