Wenda Zhou

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 22

Top Topics

Generalization Bounds

Empirical Risk Minimization

Top Venues

IEEE Trans. Inf. Theory

Publications

Li Dong, Lie Ju, Shiqi Hui, Lihua Luo, Xue Jiang, Zihan Nie, Ruiheng Zhang, Wenda Zhou, Heyan Li, Jost B. Jonas, Xin Wang, Xin Zhao, Chao He, Yuzhong Chen, Zhaohui Wang, Jianxiong Gao, Zongyuan Ge, Wenbin Wei, Dongmei Li
Retinal photograph-based deep learning system for detection of hyperthyroidism: a multicenter, diagnostic study. J. Big Data 10 (1) (2023)
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
Vitruvion: A Generative Model of Parametric CAD Sketches. ICLR (2022)
Wenda Zhou, Shirin Jalali, Arian Maleki
Compressed Sensing in the Presence of Speckle Noise. IEEE Trans. Inf. Theory 68 (10) (2022)
Wenda Zhou, Li Dong, Kai Zhang, Qian Wang, Lei Shao, Qiong Yang, Yue-Ming Liu, Li-Jian Fang, Xu-Han Shi, Chuan Zhang, Rui-Heng Zhang, He-Yan Li, Hao-Tian Wu, Wen-Bin Wei
Deep Learning for Automatic Detection of Recurrent Retinal Detachment after Surgery Using Ultra-Widefield Fundus Images: A Single-Center Study. Adv. Intell. Syst. 4 (9) (2022)
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
Vitruvion: A Generative Model of Parametric CAD Sketches. CoRR (2021)
Erik H. Thiede, Wenda Zhou, Risi Kondor
Autobahn: Automorphism-based Graph Neural Nets. NeurIPS (2021)
Wenda Zhou, Shirin Jalali, Arian Maleki
Compressed sensing in the presence of speckle noise. CoRR (2021)
Erik Henning Thiede, Wenda Zhou, Risi Kondor
Autobahn: Automorphism-based Graph Neural Nets. CoRR (2021)
Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. CoRR (2020)
Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. AISTATS (2020)
Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design. CoRR (2020)
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS (2019)
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Discrete Object Generation with Reversible Inductive Construction. CoRR (2019)
Wenda Zhou, Shirin Jalali
Towards theoretically-founded learning-based denoising. ISIT (2019)
Wenda Zhou, Shirin Jalali
Denoising of structured random processes. CoRR (2019)
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Discrete Object Generation with Reversible Inductive Construction. NeurIPS (2019)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) (2019)
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. CoRR (2018)
Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab S. Mirrokni
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems. CoRR (2018)
Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR (2018)
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. ICML (2018)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Compressibility and Generalization in Large-Scale Deep Learning. CoRR (2018)