Aamand, Anders

12 publications

ICML 2025 Breaking the $n^1.5$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrović, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu
NeurIPS 2025 Differentially Private Gomory-Hu Trees Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrović, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu
NeurIPS 2025 Differentially Private Quantiles with Smaller Error Jacob Imola, Fabrizio Boninsegna, Hannah Keller, Anders Aamand, Amrita Roy Chowdhury, Rasmus Pagh
ICML 2025 Improved Approximations for Hard Graph Problems Using Predictions Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu
ICLR 2025 Learning-Augmented Frequent Directions Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu
ICML 2025 Lightweight Protocols for Distributed Private Quantile Estimation Anders Aamand, Fabrizio Boninsegna, Abigail Gentle, Jacob Imola, Rasmus Pagh
NeurIPS 2024 Statistical-Computational Trade-Offs for Density Estimation Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Haike Xu
NeurIPS 2023 Constant Approximation for Individual Preference Stable Clustering Anders Aamand, Justin Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang
ICML 2023 Data Structures for Density Estimation Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal
NeurIPS 2023 Improved Frequency Estimation Algorithms with and Without Predictions Anders Aamand, Justin Chen, Huy Nguyen, Sandeep Silwal, Ali Vakilian
NeurIPS 2022 (Optimal) Online Bipartite Matching with Degree Information Anders Aamand, Justin Chen, Piotr Indyk
NeurIPS 2022 Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks Anders Aamand, Justin Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, Tal Wagner