Research and practice of time-sharing device sharing mechanism in remote embedded experiment platform. - Kaiwen Wu; Yanmei Jin (ICNC-FSKD, 2016)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (ICML, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. - Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

On Minimax Optimality of GANs for Robust Mean Estimation. - Kaiwen Wu; Gavin Weiguang Ding; Ruitong Huang; Yaoliang Yu (AISTATS, 2020)

Stronger and Faster Wasserstein Adversarial Attacks. - Kaiwen Wu; Allen Houze Wang; Yaoliang Yu (ICML, 2020)

Newton-type Methods for Minimax Optimization. - Guojun Zhang; Kaiwen Wu; Pascal Poupart; Yaoliang Yu (CoRR, 2020)

Research on Ultrasonic Image Segmentation of Thyroid Nodules Based on Improved U-net++. - Chaoyi Chen; Bo Xu 019; Ying Wu; Kaiwen Wu; Cuier Tan (BIC, 2022)

Local Bayesian optimization via maximizing probability of descent. - Quan Nguyen; Kaiwen Wu; Jacob R. Gardner; Roman Garnett (NeurIPS, 2022)

Dynamic adversarial domain adaptation based on multikernel maximum mean discrepancy for breast ultrasound image classification. - Bo Xu 019; Kaiwen Wu; Ying Wu; Jie He; Chaoyi Chen (Expert Syst. Appl., 2022)

Resident-Based Store Recommendation Model for Community Commercial Planning. - Kaiwen Wu; Yanhu Li; Xiaofeng He (ADMA (1), 2023)

Multi-scale Residual Spatial-Spectral Attention Longan-Litchi Extraction Network for Cultivated Land Non-grain Monitoring. - Yingshan Xue; Yonghua Jiang 001; Chengjun Wang; Kaiwen Wu; Xiaoxiao Zhang; Weiling Liu (Agro-Geoinformatics, 2023)

Crop Type Classification Network for Hyperspectral Images Coupled with Agricultural Vegetation Indices. - Shuai Zhang; Yonghua Jiang 001; Chengjun Wang; Weiling Liu; Da Li; Kaiwen Wu (Agro-Geoinformatics, 2023)

Discovering Many Diverse Solutions with Bayesian Optimization. - Natalie Maus; Kaiwen Wu; David Eriksson; Jacob R. Gardner (AISTATS, 2023)

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. - Kyurae Kim; Kaiwen Wu; Jisu Oh; Jacob R. Gardner (ICML, 2023)

On the Convergence of Black-Box Variational Inference. - Kyurae Kim; Jisu Oh; Kaiwen Wu; Yi-An Ma; Jacob R. Gardner (NeurIPS, 2023)

The Behavior and Convergence of Local Bayesian Optimization. - Kaiwen Wu; Kyurae Kim; Roman Garnett; Jacob R. Gardner (NeurIPS, 2023)

Variational Gaussian Processes with Decoupled Conditionals. - Xinran Zhu; Kaiwen Wu; Natalie Maus; Jacob R. Gardner; David Bindel (NeurIPS, 2023)

Black-Box Variational Inference Converges. - Kyurae Kim; Kaiwen Wu; Jisu Oh; Yi-An Ma; Jacob R. Gardner (CoRR, 2023)

Large-Scale Gaussian Processes via Alternating Projection. - Kaiwen Wu; Jonathan Wenger; Haydn Thomas Jones; Geoff Pleiss; Jacob R. Gardner (AISTATS, 2024)

Understanding Stochastic Natural Gradient Variational Inference. - Kaiwen Wu; Jacob R. Gardner (ICML, 2024)

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. - Jonathan Wenger; Kaiwen Wu; Philipp Hennig; Jacob R. Gardner; Geoff Pleiss; John P. Cunningham (NeurIPS, 2024)

A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. - Kaiwen Wu; Jacob R. Gardner (CoRR, 2024)

Estimating the Material Footprint at the National Level from 1993 to 2022 Based on Multi-Feature CNN-BiLSTM. - Lizhi Miao; Yannan Wang; Kaiwen Wu; Lei Huang; Mei-Po Kwan (ISPRS Int. J. Geo Inf., 2025)

Mixed Likelihood Variational Gaussian Processes. - Kaiwen Wu; Craig Sanders; Benjamin Letham; Phillip Guan (CoRR, 2025)