Pasin Manurangsi

55 papers · 2017–2026 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🏃 Academic Marathon (8) 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (9) 🐝 Cross-Pollinator (10) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (52) 🧭 Keyword Pioneer 🤝 Dynamic Duo (32) 👑 Triple Crown 🧬 Topic Evolution 🏆 Grand Slam 🔬 Deep Specialist (25) 🏆 Keyword Champion (2) 🚀 Conference Pioneer ⚡ Prolific Year (10) 🗃️ Keyword Collector (195) ❓ The Questioner (2) 💎 Century Club (53) 🔥 Unstoppable (7)

Conferences

NIPS (16) IJCAI (10) AAAI (8) ICML (7) COLT (6) AISTATS (3) ICLR (3) ALT (1) EMNLP (1)

Top co-authors

Badih Ghazi (32) Ravi Kumar (32) Pritish Kamath (19) Chiyuan Zhang (13) Warut Suksompong (12) Amer Sinha (6) Ilias Diakonikolas (4) Lynn Chua (4) Raghu Meka (3) Yangsibo Huang (3)

Research topics

Differential Privacy (7) Privacy (1)

Keywords

differential privacy (23) fair division (8) approximation algorithm (8) pac learning (4) envy-free allocation (4) combinatorial optimization (3) stochastic gradient descent (3) resource allocation (3) convex optimization (3) computational complexity (3) randomized response (2) graph theory (2) rank aggregation (2) adversarial robustness (2) social choice (2) k-means clustering (2) halfspace learning (2) asymptotic analysis (2) mechanism design (2) statistical estimation (2)

Papers

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