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Kaiwen Wu
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2020 – today
- 2024
- [c16]Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. AISTATS 2024: 2620-2628 - [c15]Kaiwen Wu, Jacob R. Gardner:
Understanding Stochastic Natural Gradient Variational Inference. ICML 2024 - [i13]Kaiwen Wu, Jacob R. Gardner:
Understanding Stochastic Natural Gradient Variational Inference. CoRR abs/2406.01870 (2024) - [i12]Kaiwen Wu, Jacob R. Gardner:
A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. CoRR abs/2407.10449 (2024) - [i11]Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham:
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. CoRR abs/2411.01036 (2024) - 2023
- [c14]Kaiwen Wu, Yanhu Li, Xiaofeng He:
Resident-Based Store Recommendation Model for Community Commercial Planning. ADMA (1) 2023: 818-830 - [c13]Yingshan Xue, Yonghua Jiang, Chengjun Wang, Kaiwen Wu, Xiaoxiao Zhang, Weiling Liu:
Multi-scale Residual Spatial-Spectral Attention Longan-Litchi Extraction Network for Cultivated Land Non-grain Monitoring. Agro-Geoinformatics 2023: 1-5 - [c12]Shuai Zhang, Yonghua Jiang, Chengjun Wang, Weiling Liu, Da Li, Kaiwen Wu:
Crop Type Classification Network for Hyperspectral Images Coupled with Agricultural Vegetation Indices. Agro-Geoinformatics 2023: 1-6 - [c11]Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS 2023: 1779-1798 - [c10]Kyurae Kim
, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. ICML 2023: 16853-16876 - [c9]Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner:
On the Convergence of Black-Box Variational Inference. NeurIPS 2023 - [c8]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. NeurIPS 2023 - [c7]Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob R. Gardner, David Bindel:
Variational Gaussian Processes with Decoupled Conditionals. NeurIPS 2023 - [i10]Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. CoRR abs/2303.10472 (2023) - [i9]Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner:
Black-Box Variational Inference Converges. CoRR abs/2305.15349 (2023) - [i8]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. CoRR abs/2305.15572 (2023) - [i7]Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. CoRR abs/2310.17137 (2023) - 2022
- [j1]Bo Xu, Kaiwen Wu, Ying Wu, Jie He, Chaoyi Chen:
Dynamic adversarial domain adaptation based on multikernel maximum mean discrepancy for breast ultrasound image classification. Expert Syst. Appl. 207: 117978 (2022) - [c6]Chaoyi Chen, Bo Xu, Ying Wu, Kaiwen Wu, Cuier Tan
Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++. BIC 2022: 532-536 - [c5]Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett:
Local Bayesian optimization via maximizing probability of descent. NeurIPS 2022 - [i6]Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. CoRR abs/2210.10953 (2022) - [i5]Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett:
Local Bayesian optimization via maximizing probability of descent. CoRR abs/2210.11662 (2022) - 2020
- [c4]Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu:
On Minimax Optimality of GANs for Robust Mean Estimation. AISTATS 2020: 4541-4551 - [c3]Kaiwen Wu, Allen Houze Wang, Yaoliang Yu:
Stronger and Faster Wasserstein Adversarial Attacks. ICML 2020: 10377-10387 - [i4]Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu:
Newton-type Methods for Minimax Optimization. CoRR abs/2006.14592 (2020) - [i3]Kaiwen Wu, Allen Houze Wang, Yaoliang Yu:
Stronger and Faster Wasserstein Adversarial Attacks. CoRR abs/2008.02883 (2020)
2010 – 2019
- 2019
- [c2]Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. ICML 2019: 4424-4434 - [i2]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. CoRR abs/1905.06125 (2019) - [i1]Kaiwen Wu, Yaoliang Yu:
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. CoRR abs/1907.11780 (2019) - 2016
- [c1]Kaiwen Wu, Yanmei Jin:
Research and practice of time-sharing device sharing mechanism in remote embedded experiment platform. ICNC-FSKD 2016: 840-844
Coauthor Index
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