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2020 – today
- 2024
[c35]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. ICML 2024- [c34]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. ICML 2024 - [c33]Praneeth Kacham, Vahab Mirrokni, Peilin Zhong:
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. ICML 2024 - [c32]Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA 2024: 3960-3996 - [c31]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: 3997-4061 - [c30]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: 1911-1922 - [i33]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 abs/2402.14950 (2024) - [i32]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR abs/2403.20307 (2024) - [i31]Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR abs/2406.02910 (2024) - [i30]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR abs/2406.04868 (2024) - [i29]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 abs/2406.11206 (2024) - [i28]Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong:
Massively Parallel Minimum Spanning Tree in General Metric Spaces. CoRR abs/2408.06455 (2024) - 2023
- [j1]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): 149:1-149:26 (2023) - [c29]Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS 2023: 48:1-48:24 - [c28]Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
k-Means Clustering with Distance-Based Privacy. NeurIPS 2023 - [c27]David P. Woodruff, Peilin Zhong, Samson Zhou:
Near-Optimal k-Clustering in the Sliding Window Model. NeurIPS 2023 - [c26]AmirMohsen Ahanchi, Alexandr Andoni, MohammadTaghi Hajiaghayi, Marina Knittel, Peilin Zhong:
Massively Parallel Tree Embeddings for High Dimensional Spaces. SPAA 2023: 77-88 - [c25]Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong:
Brief Announcement: Streaming Balanced Clustering. SPAA 2023: 311-314 - [i27]Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR abs/2301.05605 (2023) - [i26]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 abs/2304.07210 (2023) - [i25]Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong:
Differentially Private Clustering in Data Streams. CoRR abs/2307.07449 (2023) - [i24]Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR abs/2308.00503 (2023) - [i23]Praneeth Kacham, Vahab Mirrokni, Peilin Zhong:
PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. CoRR abs/2310.01655 (2023) - [i22]David P. Woodruff, Peilin Zhong, Samson Zhou:
Near-Optimal k-Clustering in the Sliding Window Model. CoRR abs/2311.00642 (2023) - 2022
- [c24]Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel k-Means Clustering for Perturbation Resilient Instances. ICML 2022: 4180-4201 - [c23]CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong:
Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS 2022 - [c22]Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS 2022 - [c21]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS 2022 - [c20]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. SODA 2022: 1613-1660 - [c19]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. SODA 2022: 3005-3042 - [i21]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR abs/2207.06944 (2022) - [i20]CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong:
Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR abs/2212.02635 (2022) - 2021
- [b1]Peilin Zhong:
New Primitives for Tackling Graph Problems and Their Applications in Parallel Computing. Columbia University, USA, 2021 - [c18]Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong:
Almost Linear Time Density Level Set Estimation via DBSCAN. AAAI 2021: 7349-7357 - [i19]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. CoRR abs/2106.02685 (2021) - [i18]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. CoRR abs/2110.15533 (2021) - 2020
- [c17]Chang Xiao, Peilin Zhong, Changxi Zheng:
Enhancing Adversarial Defense by k-Winners-Take-All. ICLR 2020 - [c16]Ruosong Wang, Peilin Zhong, Simon S. Du, Ruslan Salakhutdinov, Lin F. Yang:
Planning with General Objective Functions: Going Beyond Total Rewards. NeurIPS 2020 - [c15]Sixue Cliff Liu, Robert E. Tarjan, Peilin Zhong:
Connected Components on a PRAM in Log Diameter Time. SPAA 2020: 359-369 - [c14]Alexandr Andoni, Clifford Stein, Peilin Zhong:
Parallel approximate undirected shortest paths via low hop emulators. STOC 2020: 322-335 - [i17]S. Cliff Liu, Robert E. Tarjan, Peilin Zhong:
Connected Components on a PRAM in Log Diameter Time. CoRR abs/2003.00614 (2020) - [i16]Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 𝓁1-Norm Loss. CoRR abs/2004.07986 (2020)
2010 – 2019
- 2019
- [c13]Alexandr Andoni, Clifford Stein, Peilin Zhong:
Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. ICALP 2019: 14:1-14:16 - [c12]Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. NeurIPS 2019: 828-838 - [c11]Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng:
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. NeurIPS 2019: 2086-2097 - [c10]Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Column Subset Selection. NeurIPS 2019: 6120-6131 - [c9]Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 퓁1-Norm Loss. NeurIPS 2019: 10111-10121 - [c8]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. SODA 2019: 2772-2789 - [i15]Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng:
Rethinking Generative Coverage: A Pointwise Guaranteed Approach. CoRR abs/1902.04697 (2019) - [i14]Alexandr Andoni, Clifford Stein, Peilin Zhong:
Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. CoRR abs/1905.00850 (2019) - [i13]Chang Xiao, Peilin Zhong, Changxi Zheng:
Resisting Adversarial Attacks by k-Winners-Take-All. CoRR abs/1905.10510 (2019) - [i12]Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong:
Streaming Balanced Clustering. CoRR abs/1910.00788 (2019) - [i11]Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. CoRR abs/1910.01788 (2019) - [i10]Alexandr Andoni, Clifford Stein, Peilin Zhong:
Parallel Approximate Undirected Shortest Paths Via Low Hop Emulators. CoRR abs/1911.01956 (2019) - 2018
- [c7]Alexandr Andoni, Zhao Song, Clifford Stein, Zhengyu Wang, Peilin Zhong:
Parallel Graph Connectivity in Log Diameter Rounds. FOCS 2018: 674-685 - [c6]Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong:
Subspace Embedding and Linear Regression with Orlicz Norm. ICML 2018: 224-233 - [c5]Chang Xiao, Peilin Zhong, Changxi Zheng:
BourGAN: Generative Networks with Metric Embeddings. NeurIPS 2018: 2275-2286 - [i9]Zhao Song, Lin F. Yang, Peilin Zhong:
Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. CoRR abs/1802.00459 (2018) - [i8]Alexandr Andoni, Clifford Stein, Zhao Song, Zhengyu Wang, Peilin Zhong:
Parallel Graph Connectivity in Log Diameter Rounds. CoRR abs/1805.03055 (2018) - [i7]Chang Xiao, Peilin Zhong, Changxi Zheng:
BourGAN: Generative Networks with Metric Embeddings. CoRR abs/1805.07674 (2018) - [i6]Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong:
Subspace Embedding and Linear Regression with Orlicz Norm. CoRR abs/1806.06430 (2018) - [i5]Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Entrywise Low Rank Approximation. CoRR abs/1811.01442 (2018) - [i4]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [c4]Zhao Song, David P. Woodruff, Peilin Zhong:
Low rank approximation with entrywise l1-norm error. STOC 2017: 688-701 - [i3]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. CoRR abs/1704.08246 (2017) - 2016
- [c3]David P. Woodruff, Peilin Zhong:
Distributed low rank approximation of implicit functions of a matrix. ICDE 2016: 847-858 - [c2]Christos Boutsidis, David P. Woodruff, Peilin Zhong:
Optimal principal component analysis in distributed and streaming models. STOC 2016: 236-249 - [i2]David P. Woodruff, Peilin Zhong:
Distributed Low Rank Approximation of Implicit Functions of a Matrix. CoRR abs/1601.07721 (2016) - [i1]Zhao Song, David P. Woodruff, Peilin Zhong:
Low Rank Approximation with Entrywise ℓ1-Norm Error. CoRR abs/1611.00898 (2016) - 2014
- [c1]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: 1626-1630
Coauthor Index
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last updated on 2024-09-25 00:44 CEST by the dblp team
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