Dongxia Wu

According to our database, Dongxia Wu authored at least 16 papers between 2021 and 2024.

Collaborative distances:

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

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

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

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

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

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

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

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

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

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

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

DeepViFi: detecting oncoviral infections in cancer genomes using transformers.

Utkrisht Rajkumar, Sara Javadzadeh, Mihir Bafna, Dongxia Wu, Rose Yu, Jingbo Shang, Vineet Bafna

Proceedings of the BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Northbrook, Illinois, USA, August 7, 2022

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, Wei Li, Kevin Field, Dane Morgan

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

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

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