Peilin Zhong

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 64

Top Topics

Differentially Private

Massively Parallel

High Dimensional Spaces

Top Venues

Publications

Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR (2024)
Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA (2024)
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. STOC (2024)
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR (2024)
Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil, Chen Wang, Nathan White, Peilin Zhong
Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth. SODA (2024)
Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong
High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR (2024)
Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR (2024)
Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil, Chen Wang, Nathan White, Peilin Zhong
Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth. CoRR (2024)
Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR (2023)
David P. Woodruff, Peilin Zhong, Samson Zhou
Near-Optimal k-Clustering in the Sliding Window Model. NeurIPS (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. Proc. ACM Manag. Data 1 (2) (2023)
Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong
Differentially Private Clustering in Data Streams. CoRR (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. CoRR (2023)
Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. CoRR (2023)
AmirMohsen Ahanchi, Alexandr Andoni, MohammadTaghi Hajiaghayi, Marina Knittel, Peilin Zhong
Massively Parallel Tree Embeddings for High Dimensional Spaces. SPAA (2023)
David P. Woodruff, Peilin Zhong, Samson Zhou
Near-Optimal k-Clustering in the Sliding Window Model. CoRR (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS (2023)
Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong
Brief Announcement: Streaming Balanced Clustering. SPAA (2023)
Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
k-Means Clustering with Distance-Based Privacy. NeurIPS (2023)
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR (2022)
Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. SODA (2022)
Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. SODA (2022)
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS (2022)
Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS (2022)
Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong
Massively Parallel k-Means Clustering for Perturbation Resilient Instances. ICML (2022)
Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. CoRR (2021)
Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. CoRR (2021)
Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong
Almost Linear Time Density Level Set Estimation via DBSCAN. AAAI (2021)
S. Cliff Liu, Robert E. Tarjan, Peilin Zhong
Connected Components on a PRAM in Log Diameter Time. CoRR (2020)
Ruosong Wang, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, Lin F. Yang
Planning with General Objective Functions: Going Beyond Total Rewards. NeurIPS (2020)
Chang Xiao, Peilin Zhong, Changxi Zheng
Enhancing Adversarial Defense by k-Winners-Take-All. ICLR (2020)
Sixue Cliff Liu, Robert E. Tarjan, Peilin Zhong
Connected Components on a PRAM in Log Diameter Time. SPAA (2020)
Alexandr Andoni, Clifford Stein, Peilin Zhong
Parallel approximate undirected shortest paths via low hop emulators. STOC (2020)
Zhao Song, David P. Woodruff, Peilin Zhong
-Norm Loss. CoRR (2020)
Alexandr Andoni, Clifford Stein, Peilin Zhong
Parallel Approximate Undirected Shortest Paths Via Low Hop Emulators. CoRR (2019)
Alexandr Andoni, Clifford Stein, Peilin Zhong
Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. ICALP (2019)
Alexandr Andoni, Clifford Stein, Peilin Zhong
Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. CoRR (2019)
Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng
Rethinking Generative Coverage: A Pointwise Guaranteed Approach. CoRR (2019)
Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. NeurIPS (2019)
Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong
Efficient Symmetric Norm Regression via Linear Sketching. CoRR (2019)
Zhao Song, David P. Woodruff, Peilin Zhong
Relative Error Tensor Low Rank Approximation. SODA (2019)
Zhao Song, David P. Woodruff, Peilin Zhong
-Norm Loss. NeurIPS (2019)
Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong
Streaming Balanced Clustering. CoRR (2019)
Zhao Song, David P. Woodruff, Peilin Zhong
Towards a Zero-One Law for Column Subset Selection. NeurIPS (2019)
Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong
Efficient Symmetric Norm Regression via Linear Sketching. NeurIPS (2019)
Chang Xiao, Peilin Zhong, Changxi Zheng
Resisting Adversarial Attacks by k-Winners-Take-All. CoRR (2019)
Alexandr Andoni, Clifford Stein, Zhao Song, Zhengyu Wang, Peilin Zhong
Parallel Graph Connectivity in Log Diameter Rounds. CoRR (2018)
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong
Subspace Embedding and Linear Regression with Orlicz Norm. ICML (2018)
Chang Xiao, Peilin Zhong, Changxi Zheng
BourGAN: Generative Networks with Metric Embeddings. CoRR (2018)
Zhao Song, David P. Woodruff, Peilin Zhong
Towards a Zero-One Law for Entrywise Low Rank Approximation. CoRR (2018)
Chang Xiao, Peilin Zhong, Changxi Zheng
BourGAN: Generative Networks with Metric Embeddings. NeurIPS (2018)
Zhao Song, Lin F. Yang, Peilin Zhong
Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. CoRR (2018)
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong
Subspace Embedding and Linear Regression with Orlicz Norm. CoRR (2018)
Zhao Song, David P. Woodruff, Peilin Zhong
Relative Error Tensor Low Rank Approximation. Electron. Colloquium Comput. Complex. 25 (2018)
Alexandr Andoni, Zhao Song, Clifford Stein, Zhengyu Wang, Peilin Zhong
Parallel Graph Connectivity in Log Diameter Rounds. FOCS (2018)
Zhao Song, David P. Woodruff, Peilin Zhong
-norm error. STOC (2017)
Zhao Song, David P. Woodruff, Peilin Zhong
Relative Error Tensor Low Rank Approximation. CoRR (2017)
Christos Boutsidis, David P. Woodruff, Peilin Zhong
Optimal principal component analysis in distributed and streaming models. STOC (2016)
David P. Woodruff, Peilin Zhong
Distributed low rank approximation of implicit functions of a matrix. ICDE (2016)
Zhao Song, David P. Woodruff, Peilin Zhong
-Norm Error. CoRR (2016)
David P. Woodruff, Peilin Zhong
Distributed Low Rank Approximation of Implicit Functions of a Matrix. CoRR (2016)
Yan Xu, Tao Mo, Qiwei Feng, Peilin Zhong, Maode Lai, Eric I-Chao Chang
Deep learning of feature representation with multiple instance learning for medical image analysis. ICASSP (2014)