Manurangsi, Pasin

55 publications

TMLR 2026 Convex Optimization with Local Label Differential Privacy: Tight Bounds in All Privacy Regimes Lynn Chua, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Ziteng Sun, Chiyuan Zhang

IJCAI 2025 Asymptotic Analysis of Weighted Fair Division Pasin Manurangsi, Warut Suksompong, Tomohiko Yokoyama

IJCAI 2025 Asymptotic Fair Division: Chores Are Easier than Goods Pasin Manurangsi, Warut Suksompong

AISTATS 2025 Balls-and-Bins Sampling for DP-SGD Lynn Chua, Badih Ghazi, Charlie Harrison, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

IJCAI 2025 Dividing Conflicting Items Fairly Ayumi Igarashi, Pasin Manurangsi, Hirotaka Yoneda

COLT 2025 PREM: Privately Answering Statistical Queries with Relative Error Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Sushant Sachdeva

NeurIPS 2025 Private Hyperparameter Tuning with Ex-Post Guarantee Badih Ghazi, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

ICLR 2025 Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy Yangsibo Huang, Daogao Liu, Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Milad Nasr, Amer Sinha, Chiyuan Zhang

NeurIPSW 2024 Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

NeurIPS 2024 Differentially Private Optimization with Sparse Gradients Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

ICML 2024 How Private Are DP-SGD Implementations? Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

ICML 2024 Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

ICLR 2024 LabelDP-Pro: Learning with Label Differential Privacy via Projections Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

COLT 2024 On Convex Optimization with Semi-Sensitive Features Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

IJCAI 2024 Ordinal Maximin Guarantees for Group Fair Division Pasin Manurangsi, Warut Suksompong

NeurIPS 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

AAAI 2023 Differentially Private Fair Division Pasin Manurangsi, Warut Suksompong

AAAI 2023 Differentially Private Heatmaps Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan

NeurIPS 2023 On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

NeurIPS 2023 On Differentially Private Sampling from Gaussian and Product Distributions Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi

ICML 2023 On User-Level Private Convex Optimization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

NeurIPS 2023 Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru Varadaraja, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

ICLR 2023 Regression with Label Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang

NeurIPS 2023 Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

COLT 2023 Ticketed Learning–Unlearning Schemes Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

NeurIPS 2023 User-Level Differential Privacy with Few Examples per User Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

AISTATS 2022 Hardness of Learning a Single Neuron with Adversarial Label Noise Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren

NeurIPS 2022 Anonymized Histograms in Intermediate Privacy Models Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

NeurIPS 2022 Cryptographic Hardness of Learning Halfspaces with Massart Noise Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren

ICML 2022 Faster Privacy Accounting via Evolving Discretization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

IJCAI 2022 Fixing Knockout Tournaments with Seeds Pasin Manurangsi, Warut Suksompong

NeurIPS 2022 Private Isotonic Regression Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

AAAI 2022 Private Rank Aggregation in Central and Local Models Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

COLT 2022 Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari, Pasin Manurangsi, Ameya Velingker

AAAI 2022 The Price of Justified Representation Edith Elkind, Piotr Faliszewski, Ayumi Igarashi, Pasin Manurangsi, Ulrike Schmidt-Kraepelin, Warut Suksompong

AISTATS 2021 Robust and Private Learning of Halfspaces Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

IJCAI 2021 Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory Pasin Manurangsi, Warut Suksompong

NeurIPS 2021 Contextual Recommendations and Low-Regret Cutting-Plane Algorithms Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, Renato Leme, Jon Schneider

NeurIPS 2021 Deep Learning with Label Differential Privacy Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

ICML 2021 Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha

IJCAI 2021 Generalized Kings and Single-Elimination Winners in Random Tournaments Pasin Manurangsi, Warut Suksompong

ICML 2021 Locally Private K-Means in One Round Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

ALT 2021 Near-Tight Closure Bounds for the Littlestone and Threshold Dimensions Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

COLT 2021 On Avoiding the Union Bound When Answering Multiple Differentially Private Queries Badih Ghazi, Ravi Kumar, Pasin Manurangsi

NeurIPS 2021 User-Level Differentially Private Learning via Correlated Sampling Badih Ghazi, Ravi Kumar, Pasin Manurangsi

NeurIPS 2020 Differentially Private Clustering: Tight Approximation Ratios Badih Ghazi, Ravi Kumar, Pasin Manurangsi

ICML 2020 Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh

NeurIPS 2020 The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

IJCAI 2020 Tight Approximation for Proportional Approval Voting Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski, Krzysztof Sornat

AAAI 2019 Approximation and Hardness of Shift-Bribery Piotr Faliszewski, Pasin Manurangsi, Krzysztof Sornat

NeurIPS 2019 Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi

IJCAI 2019 The Price of Fairness for Indivisible Goods Xiaohui Bei, Xinhang Lu, Pasin Manurangsi, Warut Suksompong

AAAI 2019 When Do Envy-Free Allocations Exist? Pasin Manurangsi, Warut Suksompong

IJCAI 2017 Computing an Approximately Optimal Agreeable Set of Items Pasin Manurangsi, Warut Suksompong

COLT 2017 Inapproximability of VC Dimension and Littlestone’s Dimension Pasin Manurangsi, Aviad Rubinstein