Pasin Manurangsi

Fair Allocation of Indivisible Goods with Variable Groups AAAI 2026 Improved Differentially Private Algorithms for Rank Aggregation AAAI 2026 Asymptotic Analysis of Weighted Fair Division IJCAI 2025 Dividing Conflicting Items Fairly IJCAI 2025 Balls-and-Bins Sampling for DP-SGD AISTATS 2025 PREM: Privately Answering Statistical Queries with Relative Error COLT 2025 Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy ICLR 2025 Asymptotic Fair Division: Chores Are Easier Than Goods IJCAI 2025 Differentially Private Optimization with Sparse Gradients NIPS 2024 LabelDP-Pro: Learning with Label Differential Privacy via Projections ICLR 2024 On Convex Optimization with Semi-Sensitive Features COLT 2024 Ordinal Maximin Guarantees for Group Fair Division IJCAI 2024 Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization ICML 2024 How Private are DP-SGD Implementations? ICML 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling NIPS 2024 Differentially Private Heatmaps AAAI 2023 Sparsity-Preserving Differentially Private Training of Large Embedding Models NIPS 2023 User-Level Differential Privacy With Few Examples Per User NIPS 2023 On Computing Pairwise Statistics with Local Differential Privacy NIPS 2023 Optimal Unbiased Randomizers for Regression with Label Differential Privacy NIPS 2023 On Differentially Private Sampling from Gaussian and Product Distributions NIPS 2023 Differentially Private Fair Division AAAI 2023 Ticketed Learning–Unlearning Schemes COLT 2023 Regression with Label Differential Privacy ICLR 2023 On User-Level Private Convex Optimization ICML 2023 Private Robust Estimation by Stabilizing Convex Relaxations COLT 2022 Fixing Knockout Tournaments With Seeds IJCAI 2022 Private Rank Aggregation in Central and Local Models AAAI 2022 The Price of Justified Representation AAAI 2022 Large-Scale Differentially Private BERT EMNLP 2022 Hardness of Learning a Single Neuron with Adversarial Label Noise AISTATS 2022 Faster Privacy Accounting via Evolving Discretization ICML 2022 Private Isotonic Regression NIPS 2022 Anonymized Histograms in Intermediate Privacy Models NIPS 2022 Cryptographic Hardness of Learning Halfspaces with Massart Noise NIPS 2022 Contextual Recommendations and Low-Regret Cutting-Plane Algorithms NIPS 2021 Generalized Kings and Single-Elimination Winners in Random Tournaments IJCAI 2021 Almost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory IJCAI 2021 On Avoiding the Union Bound When Answering Multiple Differentially Private Queries COLT 2021 User-Level Differentially Private Learning via Correlated Sampling NIPS 2021 Locally Private k-Means in One Round ICML 2021 Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message ICML 2021 Deep Learning with Label Differential Privacy NIPS 2021 Near-tight Closure Bounds for the Littlestone and Threshold Dimensions ALT 2021 Robust and Private Learning of Halfspaces AISTATS 2021 Differentially Private Clustering: Tight Approximation Ratios NIPS 2020 The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise NIPS 2020 Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead ICML 2020 Tight Approximation for Proportional Approval Voting IJCAI 2020 The Price of Fairness for Indivisible Goods IJCAI 2019 Approximation and Hardness of Shift-Bribery AAAI 2019 When Do Envy-Free Allocations Exist? AAAI 2019 Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin NIPS 2019 Computing an Approximately Optimal Agreeable Set of Items IJCAI 2017 Inapproximability of VC Dimension and Littlestone’s Dimension COLT 2017