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

A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations. - Mingren Shen; Guanzhao Li; Dongxia Wu; Yudai Yaguchi; Jack C. Haley; Kevin G. Field; Dane Morgan (CoRR, 2021)

Multi defect detection and analysis of electron microscopy images with deep learning. - Mingren Shen; Guanzhao Li; Dongxia Wu; Yuhan Liu; Jacob Greaves; Wei Hao; Nathaniel J. Krakauer; Leah Krudy; Jacob Perez; Varun Sreenivasan; Bryan Sanchez; Oigimer Torres; et al. (CoRR, 2021)

DeepViFi: detecting oncoviral infections in cancer genomes using transformers. - Utkrisht Rajkumar; Sara Javadzadeh; Mihir Bafna; Dongxia Wu; Rose Yu; Jingbo Shang; Vineet Bafna (BCB, 2022)

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

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)

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)

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

MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling. - Peter Eckmann; Dongxia Wu; Germano Heinzelmann; Michael K. Gilson; Rose Yu (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)

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs. - Ruijia Niu; Dongxia Wu; Rose Yu; Yi-An Ma (CoRR, 2024)

MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning. - Peter Eckmann; Dongxia Wu; Germano Heinzelmann; Michael K. Gilson; 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)