Deep learning of feature representation with multiple instance learning for medical image analysis. - Yan Xu 001; Tao Mo; Qiwei Feng; Peilin Zhong; Maode Lai; Eric I-Chao Chang (ICASSP, 2014)

Distributed low rank approximation of implicit functions of a matrix. - David P. Woodruff; Peilin Zhong (ICDE, 2016)

Optimal principal component analysis in distributed and streaming models. - Christos Boutsidis; David P. Woodruff; Peilin Zhong (STOC, 2016)

Low rank approximation with entrywise l1-norm error. - Zhao Song 002; David P. Woodruff; Peilin Zhong (STOC, 2017)

Parallel Graph Connectivity in Log Diameter Rounds. - Alexandr Andoni; Zhao Song 002; Clifford Stein 001; Zhengyu Wang; Peilin Zhong (FOCS, 2018)

Subspace Embedding and Linear Regression with Orlicz Norm. - Alexandr Andoni; Chengyu Lin 001; Ying Sheng 004; Peilin Zhong; Ruiqi Zhong (ICML, 2018)

BourGAN: Generative Networks with Metric Embeddings. - Chang Xiao; Peilin Zhong; Changxi Zheng (NeurIPS, 2018)

Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to k-Clustering. - Zhao Song 002; Lin F. Yang; Peilin Zhong (CoRR, 2018)

Towards a Zero-One Law for Entrywise Low Rank Approximation. - Zhao Song 002; David P. Woodruff; Peilin Zhong (CoRR, 2018)

Relative Error Tensor Low Rank Approximation. - Zhao Song 002; David P. Woodruff; Peilin Zhong (Electron. Colloquium Comput. Complex., 2018)

Log Diameter Rounds Algorithms for 2-Vertex and 2-Edge Connectivity. - Alexandr Andoni; Clifford Stein 001; Peilin Zhong (ICALP, 2019)

Efficient Symmetric Norm Regression via Linear Sketching. - Zhao Song 002; Ruosong Wang; Lin F. Yang; Hongyang Zhang 001; Peilin Zhong (NeurIPS, 2019)

Towards a Zero-One Law for Column Subset Selection. - Zhao Song 002; David P. Woodruff; Peilin Zhong (NeurIPS, 2019)

Average Case Column Subset Selection for Entrywise 퓁1-Norm Loss. - Zhao Song 002; David P. Woodruff; Peilin Zhong (NeurIPS, 2019)

Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. - Peilin Zhong; Yuchen Mo; Chang Xiao; Pengyu Chen; Changxi Zheng (NeurIPS, 2019)

Rethinking Generative Coverage: A Pointwise Guaranteed Approach. - Peilin Zhong; Yuchen Mo; Chang Xiao; Pengyu Chen; Changxi Zheng (CoRR, 2019)

Resisting Adversarial Attacks by k-Winners-Take-All. - Chang Xiao; Peilin Zhong; Changxi Zheng (CoRR, 2019)

Streaming Balanced Clustering. - Hossein Esfandiari; Vahab S. Mirrokni; Peilin Zhong (CoRR, 2019)

Enhancing Adversarial Defense by k-Winners-Take-All. - Chang Xiao; Peilin Zhong; Changxi Zheng (ICLR, 2020)

Planning with General Objective Functions: Going Beyond Total Rewards. - Ruosong Wang; Peilin Zhong; Simon S. Du; Ruslan Salakhutdinov; Lin F. Yang (NeurIPS, 2020)

Connected Components on a PRAM in Log Diameter Time. - Sixue Cliff Liu; Robert E. Tarjan; Peilin Zhong (SPAA, 2020)

Parallel approximate undirected shortest paths via low hop emulators. - Alexandr Andoni; Clifford Stein 001; Peilin Zhong (STOC, 2020)

Average Case Column Subset Selection for Entrywise 𝓁1-Norm Loss. - Zhao Song 002; David P. Woodruff; Peilin Zhong (CoRR, 2020)

Almost Linear Time Density Level Set Estimation via DBSCAN. - Hossein Esfandiari; Vahab S. Mirrokni; Peilin Zhong (AAAI, 2021)

New Primitives for Tackling Graph Problems and Their Applications in Parallel Computing. - Peilin Zhong (2021)

Massively Parallel k-Means Clustering for Perturbation Resilient Instances. - Vincent Cohen-Addad; Vahab S. Mirrokni; Peilin Zhong (ICML, 2022)

Stars: Tera-Scale Graph Building for Clustering and Learning. - CJ Carey; Jonathan Halcrow; Rajesh Jayaram; Vahab Mirrokni; Warren Schudy; Peilin Zhong (NeurIPS, 2022)

Near-Optimal Private and Scalable $k$-Clustering. - Vincent Cohen-Addad; Alessandro Epasto; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong (NeurIPS, 2022)

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. - Alessandro Epasto; Vahab Mirrokni; Bryan Perozzi; Anton Tsitsulin; Peilin Zhong (NeurIPS, 2022)

Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. - Alessandro Epasto; Mohammad Mahdian; Vahab S. Mirrokni; Peilin Zhong (SODA, 2022)

Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. - Alessandro Epasto; Mohammad Mahdian; Vahab S. Mirrokni; Peilin Zhong (SODA, 2022)

Stars: Tera-Scale Graph Building for Clustering and Graph Learning. - CJ Carey; Jonathan Halcrow; Rajesh Jayaram; Vahab Mirrokni; Warren Schudy; Peilin Zhong (CoRR, 2022)

Differentially Private Continual Releases of Streaming Frequency Moment Estimations. - Alessandro Epasto; Jieming Mao; Andres Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (ITCS, 2023)

k-Means Clustering with Distance-Based Privacy. - Alessandro Epasto; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong (NeurIPS, 2023)

Near-Optimal k-Clustering in the Sliding Window Model. - David P. Woodruff; Peilin Zhong; Samson Zhou (NeurIPS, 2023)

Massively Parallel Tree Embeddings for High Dimensional Spaces. - AmirMohsen Ahanchi; Alexandr Andoni; MohammadTaghi Hajiaghayi; Marina Knittel; Peilin Zhong (SPAA, 2023)

Brief Announcement: Streaming Balanced Clustering. - Hossein Esfandiari; Vahab Mirrokni; Peilin Zhong (SPAA, 2023)

Differentially Private Clustering in Data Streams. - Alessandro Epasto; Tamalika Mukherjee; Peilin Zhong (CoRR, 2023)

PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. - Praneeth Kacham; Vahab Mirrokni; Peilin Zhong (CoRR, 2023)

Measuring Re-identification Risk. - CJ Carey; Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Shankar Kumar; Andres Muñoz Medina; Vahab Mirrokni; Gabriel Henrique Nunes; Sergei Vassilvitskii; Peilin Zhong (Proc. ACM Manag. Data, 2023)

Perturb-and-Project: Differentially Private Similarities and Marginals. - Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Vahab Mirrokni; Peilin Zhong (ICML, 2024)

High-Dimensional Geometric Streaming for Nearly Low Rank Data. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (ICML, 2024)

PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. - Praneeth Kacham; Vahab Mirrokni; Peilin Zhong (ICML, 2024)

Parallel Approximate Maximum Flows in Near-Linear Work and Polylogarithmic Depth. - Arpit Agarwal; Sanjeev Khanna; Huan Li 002; Prathamesh Patil; Chen Wang 027; Nathan White; Peilin Zhong (SODA, 2024)

Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. - Rajesh Jayaram; Vahab Mirrokni; Shyam Narayanan; Peilin Zhong (SODA, 2024)

Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (STOC, 2024)

Optimal Communication for Classic Functions in the Coordinator Model and Beyond. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (CoRR, 2024)

Retraining with Predicted Hard Labels Provably Increases Model Accuracy. - Rudrajit Das; Inderjit S. Dhillon; Alessandro Epasto; Adel Javanmard; Jieming Mao; Vahab Mirrokni; Sujay Sanghavi; Peilin Zhong (CoRR, 2024)

Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. - Zeman Li; Xinwei Zhang 001; Peilin Zhong; Yuan Deng; Meisam Razaviyayn; Vahab Mirrokni (ICLR, 2025)

Massively Parallel Minimum Spanning Tree in General Metric Spaces. - Amir Azarmehr; Soheil Behnezhad; Rajesh Jayaram; Jakub Lacki; Vahab Mirrokni; Peilin Zhong (SODA, 2025)

Titans: Learning to Memorize at Test Time. - Ali Behrouz; Peilin Zhong; Vahab Mirrokni (CoRR, 2025)

PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts. - Zeman Li; Yuan Deng; Peilin Zhong; Meisam Razaviyayn; Vahab Mirrokni (CoRR, 2025)

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization. - Ali Behrouz; Meisam Razaviyayn; Peilin Zhong; Vahab Mirrokni (CoRR, 2025)

Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures. - Alina Ene; Alessandro Epasto; Vahab Mirrokni; Hoai-An Nguyen; Huy L. Nguyen; David P. Woodruff; Peilin Zhong (CoRR, 2025)

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model. - Rachel Cummings; Alessandro Epasto; Jieming Mao; Tamalika Mukherjee; Tingting Ou; Peilin Zhong (CoRR, 2025)

ATLAS: Learning to Optimally Memorize the Context at Test Time. - Ali Behrouz; Zeman Li; Praneeth Kacham; Majid Daliri; Yuan Deng; Peilin Zhong; Meisam Razaviyayn; Vahab Mirrokni (CoRR, 2025)

Differentially Private Synthetic Data Release for Topics API Outputs. - Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Andrés Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (CoRR, 2025)