Anders Aamand

Postdoc@University of Copenhagen·丹麦·OpenReview

研究方向

Differential privacy · theory · graph structured data · Learning augmented algorithms · algorithms with predictions · learning theory · Hashing · sketching · streaming

6.5

Skirting Additive Error Barriers for Private Turnstile Streams

ICLR 2026Poster

一作

7.8

Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition

合作者 (18)

Anders Aamand

Skirting Additive Error Barriers for Private Turnstile Streams

Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition

Learning-Augmented Frequent Directions

Lightweight Protocols for Distributed Private Quantile Estimation

Differentially Private Gomory-Hu Trees

Differentially Private Quantiles with Smaller Error

Improved Approximations for Hard Graph Problems using Predictions