Kaiwen Wu

According to our database, Kaiwen Wu authored at least 23 papers between 2016 and 2024.

Collaborative distances:

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

2024

A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions.

Kaiwen Wu, Jacob R. Gardner

CoRR, 2024

Understanding Stochastic Natural Gradient Variational Inference.

Kaiwen Wu, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner

CoRR, 2023

Black-Box Variational Inference Converges.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner

CoRR, 2023

Variational Gaussian Processes with Decoupled Conditionals.

Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob R. Gardner, David Bindel

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Behavior and Convergence of Local Bayesian Optimization.

Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Convergence of Black-Box Variational Inference.

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

Proceedings of the International Conference on Machine Learning, 2023

Discovering Many Diverse Solutions with Bayesian Optimization.

Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Crop Type Classification Network for Hyperspectral Images Coupled with Agricultural Vegetation Indices.

Shuai Zhang, Yonghua Jiang, Chengjun Wang, Weiling Liu, Da Li, Kaiwen Wu

Proceedings of the 11th International Conference on Agro-Geoinformatics, 2023

Multi-scale Residual Spatial-Spectral Attention Longan-Litchi Extraction Network for Cultivated Land Non-grain Monitoring.

Yingshan Xue, Yonghua Jiang, Chengjun Wang, Kaiwen Wu, Xiaoxiao Zhang, Weiling Liu

Proceedings of the 11th International Conference on Agro-Geoinformatics, 2023

Resident-Based Store Recommendation Model for Community Commercial Planning.

Kaiwen Wu, Yanhu Li, Xiaofeng He

Proceedings of the Advanced Data Mining and Applications - 19th International Conference, 2023

2022

Dynamic adversarial domain adaptation based on multikernel maximum mean discrepancy for breast ultrasound image classification.

Bo Xu, Kaiwen Wu, Ying Wu, Jie He, Chaoyi Chen

Expert Syst. Appl., 2022

Local Bayesian optimization via maximizing probability of descent.

Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++.

Chaoyi Chen, Bo Xu, Ying Wu, Kaiwen Wu, Cuier Tan

Proceedings of the BIC 2022: 2nd International Conference on Bioinformatics and Intelligent Computing, Harbin, China, January 21, 2022

2020

Newton-type Methods for Minimax Optimization.

Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu

CoRR, 2020

Stronger and Faster Wasserstein Adversarial Attacks.

Kaiwen Wu, Allen Houze Wang, Yaoliang Yu

Proceedings of the 37th International Conference on Machine Learning, 2020

On Minimax Optimality of GANs for Robust Mean Estimation.

Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin.

Kaiwen Wu, Yaoliang Yu

CoRR, 2019

Distributional Reinforcement Learning for Efficient Exploration.

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

CoRR, 2019

Distributional Reinforcement Learning for Efficient Exploration.

Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

Proceedings of the 36th International Conference on Machine Learning, 2019

2016

Research and practice of time-sharing device sharing mechanism in remote embedded experiment platform.

Kaiwen Wu, Yanmei Jin

Proceedings of the 12th International Conference on Natural Computation, 2016