Ma, Yian

40 publications

ICLR 2025 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, Yian Ma, Biwei Huang

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

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

AISTATS 2025 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

ICML 2025 Discovering Latent Causal Graphs from Spatiotemporal Data Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yian Ma, Rose Yu

NeurIPS 2025 Efficiently Scaling LLM Reasoning Programs with Certaindex Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu, Zhongdongming Dai, Yonghao Zhuang, Yian Ma, Aurick Qiao, Tajana Rosing, Ion Stoica, Hao Zhang

ICML 2025 Learning to Steer Learners in Games Yizhou Zhang, Yian Ma, Eric Mazumdar

NeurIPS 2025 Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference Kyurae Kim, Yian Ma, Trevor Campbell, Jacob R. Gardner

NeurIPS 2025 Preference Optimization on Pareto Sets: On a Theory of Multi-Objective Optimization Abhishek Roy, Geelon So, Yian Ma

NeurIPS 2025 Purifying Approximate Differential Privacy with Randomized Post-Processing Yingyu Lin, Erchi Wang, Yian Ma, Yu-Xiang Wang

ICLRW 2025 Understanding the Sources of Uncertainty for Large Language and Multimodal Models Ziran Yang, Shibo Hao, Hao Sun, Lai Jiang, Qiyue Gao, Yian Ma, Zhiting Hu

ICMLW 2024 Accuracy on the Wrong Line: On the Pitfalls of Noisy Data for OOD Generalisation Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf

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

NeurIPSW 2024 Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yian Ma, Rose Yu

ICML 2024 Discovering Mixtures of Structural Causal Models from Time Series Data Sumanth Varambally, Yian Ma, Rose Yu

ICML 2024 Faster Sampling via Stochastic Gradient Proximal Sampler Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang

NeurIPSW 2024 GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting Mohammadmehdi Zahedi, Dongxia Wu, Jessica T. Davis, Yian Ma, Alessandro Vespignani, Rose Yu, Matteo Chinazzi

NeurIPSW 2024 GLEAM-AI: Neural Surrogate for Accelerated Epidemic Analytics and Forecasting Mohammadmehdi Zahedi, Dongxia Wu, Jessica T. Davis, Yian Ma, Alessandro Vespignani, Rose Yu, Matteo Chinazzi

AISTATS 2024 Learning Granger Causality from Instance-Wise Self-Attentive Hawkes Processes Dongxia Wu, Tsuyoshi Ide, Georgios Kollias, Jiri Navratil, Aurelie Lozano, Naoki Abe, Yian Ma, Rose Yu

AISTATS 2024 Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? Kyurae Kim, Yian Ma, Jacob 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

UAI 2024 On Convergence of Federated Averaging Langevin Dynamics Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin

ICLR 2024 Reverse Diffusion Monte Carlo Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang

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

ICMLW 2024 Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yian Ma, Tong Zhang

NeurIPSW 2024 Towards Personalized Language Models via Inference-Time Human Preference Optimization Nikki Lijing Kuang, Wei Sun, Scott McFaddin, Yian Ma, Markus Ettl

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

NeurIPSW 2024 Uncovering Latent Causal Structures from Spatiotemporal Data Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yian Ma, Rose Yu

NeurIPS 2023 Aiming Towards the Minimizers: Fast Convergence of SGD for Overparametrized Problems Chaoyue Liu, Dmitriy Drusvyatskiy, Misha Belkin, Damek Davis, Yian Ma

ICML 2023 Disentangled Multi-Fidelity Deep Bayesian Active Learning Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

ICML 2023 Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning Amin Karbasi, Nikki Lijing Kuang, Yian Ma, Siddharth Mitra

NeurIPS 2023 On the Convergence of Black-Box Variational Inference Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob Gardner

NeurIPS 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

NeurIPSW 2023 SGD Batch Saturation for Training Wide Neural Networks Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yian Ma

JMLR 2022 Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D'Amour, Katherine 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, Yian Ma, Cory 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

UAI 2021 Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

ICML 2020 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics Matthew Hoffman, Yian Ma

ICML 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

ICML 2020 On Approximate Thompson Sampling with Langevin Algorithms Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett

ICML 2018 On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan