ML Anthology

Zhong, Peilin

34 publications

ICLR 2025 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
ICML 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
ICLRW 2025 MS-SSM: A Multi-Scale State Space Model for Enhanced Sequence Modeling Mahdi Karami, Ali Behrouz, Peilin Zhong, Razvan Pascanu, Vahab Mirrokni
ICML 2025 Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy Nguyen, David Woodruff, Peilin Zhong
NeurIPS 2025 Nearly-Linear Time and Massively Parallel Algorithms for $k$-Anonymity Kevin Aydin, Honghao Lin, David Woodruff, Peilin Zhong
NeurIPS 2025 Nested Learning: The Illusion of Deep Learning Architectures Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni
NeurIPS 2025 PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni
ICLRW 2025 PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni
ICML 2025 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
NeurIPS 2025 Titans: Learning to Memorize at Test Time Ali Behrouz, Peilin Zhong, Vahab Mirrokni
NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
ICML 2024 High-Dimensional Geometric Streaming for Nearly Low Rank Data Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David Woodruff, Peilin Zhong
ICML 2024 Perturb-and-Project: Differentially Private Similarities and Marginals Vincent Cohen-Addad, Tommaso D’Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
ICML 2024 PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
NeurIPS 2023 $k$-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
NeurIPSW 2023 A New Framework for Measuring Re-Identification Risk Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong
ICMLW 2023 Differentially Private Clustering in Data Streams Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong
ICMLW 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 Woodruff, Peilin Zhong, Samson Zhou
NeurIPS 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
NeurIPSW 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
ICML 2022 Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong
NeurIPS 2022 Near-Optimal Private and Scalable $k$-Clustering Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
NeurIPS 2022 Stars: Tera-Scale Graph Building for Clustering and Learning Cj Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
AAAI 2021 Almost Linear Time Density Level Set Estimation via DBSCAN Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong
ICLR 2020 Enhancing Adversarial Defense by K-Winners-Take-All Chang Xiao, Peilin Zhong, Changxi Zheng
NeurIPS 2020 Planning with General Objective Functions: Going Beyond Total Rewards Ruosong Wang, Peilin Zhong, Simon S Du, Ruslan Salakhutdinov, Lin Yang
NeurIPS 2019 Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss Zhao Song, David Woodruff, Peilin Zhong
NeurIPS 2019 Efficient Symmetric Norm Regression via Linear Sketching Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong
NeurIPS 2019 Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng
NeurIPS 2019 Towards a Zero-One Law for Column Subset Selection Zhao Song, David Woodruff, Peilin Zhong
NeurIPS 2018 BourGAN: Generative Networks with Metric Embeddings Chang Xiao, Peilin Zhong, Changxi Zheng
ICML 2018 Subspace Embedding and Linear Regression with Orlicz Norm Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong