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Dongxia Wu
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2020 – today
- 2024
- [c7]Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS 2024: 415-423 - [c6]Ruijia Niu, Dongxia Wu, Kai Kim, Yian Ma, Duncan Watson-Parris, Rose Yu:
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. ICML 2024 - [i14]Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. CoRR abs/2402.03726 (2024) - [i13]Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K. Gilson, Rose Yu:
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling. CoRR abs/2402.10387 (2024) - [i12]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:
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR abs/2402.18012 (2024) - [i11]Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris
, Rose Yu:
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling. CoRR abs/2402.18846 (2024) - [i10]Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu:
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization. CoRR abs/2407.00610 (2024) - [i9]Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma:
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs. CoRR abs/2410.06431 (2024) - [i8]Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K. Gilson, Rose Yu:
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning. CoRR abs/2410.11226 (2024) - 2023
- [c5]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. ICML 2023: 37624-37634 - [c4]Dongxia Wu
Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD 2023: 2559-2569 - [i7]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. CoRR abs/2305.04392 (2023) - 2022
- [c3]Utkrisht Rajkumar, Sara Javadzadeh, Mihir Bafna, Dongxia Wu, Rose Yu, Jingbo Shang, Vineet Bafna:
DeepViFi: detecting oncoviral infections in cancer genomes using transformers. BCB 2022: 2:1-2:8 - [c2]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. KDD 2022: 2029-2038 - [i6]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. CoRR abs/2206.04872 (2022) - 2021
- [c1]Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD 2021: 1841-1851 - [i5]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. CoRR abs/2102.06684 (2021) - [i4]Dongxia Wu, Liyao Gao
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR abs/2105.11982 (2021) - [i3]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR abs/2106.02770 (2021) - [i2]Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G. Field, Dane Morgan:
A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations. CoRR abs/2108.08882 (2021) - [i1]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:
Multi defect detection and analysis of electron microscopy images with deep learning. CoRR abs/2108.08883 (2021)
Coauthor Index
aka: Yian Ma
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