Yi-An Ma - RankMe

74 papers A* 12A 3Journal 58Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond Mode Elicitation: Diversity-Preserving Reinforcement Learning via Latent Diffusion Reasoner. CoRR Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Yi-An Ma, Lianhui Qin
2026	J	`jnl`	Divide and Learn: Multi-Objective Combinatorial Optimization at Scale. CoRR Esha Singh, Dongxia Wu, Chien-Yi Yang, Tajana Rosing, Rose Yu, Yi-An Ma
2026	J	`jnl`	Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space. CoRR Kyurae Kim, Qiang Fu, Yi-An Ma, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	A Gradient-Based Optimization Method Using the Koopman Operator. SIAM J. Appl. Dyn. Syst. Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, Xiu Yang
2025	J	`jnl`	Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data. CoRR Haoran Deng, Yingyu Lin, Zhenghao Lin, Xiao Liu, Yizhou Sun, Yi-An Ma, Yeyun Gong
2025	J	`jnl`	LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning. CoRR Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki, Navdeep Jaitly, Yi-An Ma, Lianhui Qin
2025	J	`jnl`	Learnable Mixed Nash Equilibria are Collectively Rational. CoRR Geelon So, Yi-An Ma
2025	J	`jnl`	Learning to Steer Learners in Games. CoRR Yizhou Zhang, Yi-An Ma, Eric Mazumdar
2025	J	`jnl`	Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. CoRR Kyurae Kim, Yi-An Ma, Trevor Campbell, Jacob R. Gardner
2025	J	`jnl`	Purifying Approximate Differential Privacy with Randomized Post-processing. CoRR Yingyu Lin, Erchi Wang, Yi-An Ma, Yu-Xiang Wang
2025	J	`jnl`	SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA. CoRR Haozhou Xu, Dongxia Wu, Matteo Chinazzi, Ruijia Niu, Rose Yu, Yi-An Ma
2025	J	`jnl`	Zephyrus: An Agentic Framework for Weather Science. CoRR Sumanth Varambally, Marshall Fisher, Jas Thakker, Yiwei Chen, Zhirui Xia, Yasaman Jafari, Ruijia Niu, Manas Jain, Veeramakali Vignesh Manivannan, Zachary Novack, Luyu Han, Srikar Eranky, Salva Rühling Cachay, Taylor Berg-Kirkpatrick, Duncan Watson-Parris, Yi-An Ma, Rose Yu
2024	J	`jnl`	A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. CoRR Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yi-An Ma, Biwei Huang
2024	J	`jnl`	Demystifying SGD with Doubly Stochastic Gradients. CoRR Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner
2024	J	`jnl`	Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization. CoRR Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu
2024	J	`jnl`	Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Haorui Wang, Dongxia Wu, Aaron M. Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
2024	J	`jnl`	Discovering Latent Structural Causal Models from Spatio-Temporal Data. CoRR Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yi-An Ma, Rose Yu
2024	J	`jnl`	Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
2024	A*	`conf`	Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang
2024	J	`jnl`	Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs. CoRR Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma
2024	A	`conf`	Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu
2024	J	`jnl`	Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. CoRR Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu
2024	A	`conf`	Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2024	J	`jnl`	Log-concave Sampling over a Convex Body with a Barrier: a Robust and Unified Dikin Walk. CoRR Yuzhou Gu, Nikki Lijing Kuang, Yi-An Ma, Zhao Song, Lichen Zhang
2024	J	`jnl`	Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. CoRR Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris, Rose Yu
2024	J	`jnl`	Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang
2023	A*	`conf`	Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. NeurIPS Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma
2023	J	`jnl`	Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma
2023	J	`jnl`	Black-Box Variational Inference Converges. CoRR Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner
2023	A*	`conf`	Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2023	J	`jnl`	Discovering Mixtures of Structural Causal Models from Time Series Data. CoRR Sumanth Varambally, Yi-An Ma, Rose Yu
2023	A*	`conf`	Disentangled Multi-Fidelity Deep Bayesian Active Learning. ICML Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu
2023	J	`jnl`	Disentangled Multi-Fidelity Deep Bayesian Active Learning. CoRR Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu
2023	A*	`conf`	Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. ICML Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra
2023	J	`jnl`	Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. CoRR Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra
2023	J	`jnl`	Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2023	A*	`conf`	On the Convergence of Black-Box Variational Inference. NeurIPS Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner
2023	J	`jnl`	Optimization on Pareto sets: On a theory of multi-objective optimization. CoRR Abhishek Roy, Geelon So, Yi-An Ma
2023	J	`jnl`	Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. CoRR Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma
2023	J	`jnl`	The Adaptive Spectral Koopman Method for Dynamical Systems. SIAM J. Appl. Dyn. Syst. Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang
2022	J	`jnl`	Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. CoRR Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang
2022	A*	`conf`	Multi-fidelity Hierarchical Neural Processes. KDD Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2022	J	`jnl`	Multi-fidelity Hierarchical Neural Processes. CoRR Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2022	J	`jnl`	On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. CoRR Ruoqi Shen, Liyao Gao, Yi-An Ma
2022	J	`jnl`	Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection. CoRR Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang
2022	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 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
2022	J	`jnl`	When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. J. Mach. Learn. Res. Yoav Freund, Yi-An Ma, Tong Zhang
2021	J	`jnl`	Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2021	J	`jnl`	DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. CoRR Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2021	J	`jnl`	High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. J. Mach. Learn. Res. Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	On Convergence of Federated Averaging Langevin Dynamics. CoRR Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin
2021	A*	`conf`	Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu
2021	J	`jnl`	Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu
2021	J	`jnl`	Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. CoRR Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A. Heller, Yi-An Ma, Michael I. Jordan
2021	J	`jnl`	When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. CoRR Yoav Freund, Yi-An Ma, Tong Zhang
2020	A*	`conf`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	J	`jnl`	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran
2020	A*	`conf`	On Approximate Thompson Sampling with Langevin Algorithms. ICML Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
2020	J	`jnl`	On Thompson Sampling with Langevin Algorithms. CoRR Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR 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
2019	J	`jnl`	Bayesian Robustness: A Nonasymptotic Viewpoint. CoRR Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, Michael I. Jordan
2019	A	`conf`	Deep Mixture of Experts via Shallow Embedding. UAI Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez
2019	J	`jnl`	High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. CoRR Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Irreversible samplers from jump and continuous Markov processes. Stat. Comput. Yi-An Ma, Emily B. Fox, Tianqi Chen, Lei Wu
2019	J	`jnl`	Is There an Analog of Nesterov Acceleration for MCMC? CoRR Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2019	J	`jnl`	Stochastic Gradient MCMC for State Space Models. SIAM J. Math. Data Sci. Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox
2018	J	`jnl`	Deep Mixture of Experts via Shallow Embedding. CoRR Xin Wang, Fisher Yu, Ruth Wang, Yi-An Ma, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez
2018	A*	`conf`	On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. ICML Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2018	J	`jnl`	On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. CoRR Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2018	J	`jnl`	Sampling Can Be Faster Than Optimization. CoRR Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
2018	J	`jnl`	Stochastic Gradient MCMC for State Space Models. CoRR Christopher Aicher, Yi-An Ma, Nicholas J. Foti, Emily B. Fox
2017	A*	`conf`	Stochastic Gradient MCMC Methods for Hidden Markov Models. ICML Yi-An Ma, Nicholas J. Foti, Emily B. Fox
2015	—	`conf`	A Complete Recipe for Stochastic Gradient MCMC. NIPS Yi-An Ma, Tianqi Chen, Emily B. Fox
2014	J	`jnl`	Potential Function in a Continuous Dissipative Chaotic System: Decomposition Scheme and Role of Strange Attractor. Int. J. Bifurc. Chaos Yi-An Ma, Qijun Tan, Ruoshi Yuan, Bo Yuan, Ping Ao