One-Way Trail Orientations. - Anders Aamand; Niklas Hjuler; Jacob Holm; Eva Rotenberg (ICALP, 2018)

Power of d Choices with Simple Tabulation. - Anders Aamand; Mathias Bæk Tejs Knudsen; Mikkel Thorup (ICALP, 2018)

Non-empty Bins with Simple Tabulation Hashing. - Anders Aamand; Mikkel Thorup (SODA, 2019)

(Learned) Frequency Estimation Algorithms under Zipfian Distribution. - Anders Aamand; Piotr Indyk; Ali Vakilian (CoRR, 2019)

Fast hashing with strong concentration bounds. - Anders Aamand; Jakob Bæk Tejs Knudsen; Mathias Bæk Tejs Knudsen; Peter Michael Reichstein Rasmussen; Mikkel Thorup (STOC, 2020)

No Repetition: Fast Streaming with Highly Concentrated Hashing. - Anders Aamand; Debarati Das 001; Evangelos Kipouridis; Jakob Bæk Tejs Knudsen; Peter M. R. Rasmussen; Mikkel Thorup (CoRR, 2020)

Disks in Curves of Bounded Convex Curvature. - Anders Aamand; Mikkel Abrahamsen; Mikkel Thorup (Am. Math. Mon., 2020)

Classifying Convex Bodies by Their Contact and Intersection Graphs. - Anders Aamand; Mikkel Abrahamsen; Jakob Bæk Tejs Knudsen; Peter Michael Reichstein Rasmussen (SoCG, 2021)

Load balancing with dynamic set of balls and bins. - Anders Aamand; Jakob Bæk Tejs Knudsen; Mikkel Thorup (STOC, 2021)

(Optimal) Online Bipartite Matching with Degree Information. - Anders Aamand; Justin Y. Chen; Piotr Indyk (NeurIPS, 2022)

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks. - Anders Aamand; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Ronitt Rubinfeld; Nicholas Schiefer; Sandeep Silwal; Tal Wagner (NeurIPS, 2022)

No Repetition: Fast and Reliable Sampling with Highly Concentrated Hashing. - Anders Aamand; Debarati Das 001; Evangelos Kipouridis; Jakob Bæk Tejs Knudsen; Peter M. R. Rasmussen; Mikkel Thorup (Proc. VLDB Endow., 2022)

Optimal Decremental Connectivity in Non-Sparse Graphs. - Anders Aamand; Adam Karczmarz; Jakub Lacki; Nikos Parotsidis; Peter M. R. Rasmussen; Mikkel Thorup (ICALP, 2023)

Data Structures for Density Estimation. - Anders Aamand; Alexandr Andoni; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Sandeep Silwal (ICML, 2023)

Constant Approximation for Individual Preference Stable Clustering. - Anders Aamand; Justin Y. Chen; Allen Liu; Sandeep Silwal; Pattara Sukprasert; Ali Vakilian; Fred Zhang (NeurIPS, 2023)

Improved Frequency Estimation Algorithms with and without Predictions. - Anders Aamand; Justin Y. Chen; Huy Lê Nguyen; Sandeep Silwal; Ali Vakilian (NeurIPS, 2023)

Online Sorting and Translational Packing of Convex Polygons. - Anders Aamand; Mikkel Abrahamsen; Lorenzo Beretta 001; Linda Kleist (SODA, 2023)

Improved Space Bounds for Learning with Experts. - Anders Aamand; Justin Y. Chen; Huy Lê Nguyen; Sandeep Silwal (CoRR, 2023)

Tiling with Squares and Packing Dominos in Polynomial Time. - Anders Aamand; Mikkel Abrahamsen; Thomas D. Ahle; Peter M. R. Rasmussen (ACM Trans. Algorithms, 2023)

Statistical-Computational Trade-offs for Density Estimation. - Anders Aamand; Alexandr Andoni; Justin Y. Chen; Piotr Indyk; Shyam Narayanan; Sandeep Silwal; Haike Xu (NeurIPS, 2024)

Differentially Private Gomory-Hu Trees. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2024)

Hashing for Sampling-Based Estimation. - Anders Aamand; Ioana O. Bercea; Jakob Bæk Tejs Houen; Jonas Klausen; Mikkel Thorup (CoRR, 2024)

Near-Optimal Trace Reconstruction for Mildly Separated Strings. - Anders Aamand; Allen Liu; Shyam Narayanan (ICALP, 2025)

Learning-Augmented Frequent Directions. - Anders Aamand; Justin Y. Chen; Siddharth Gollapudi; Sandeep Silwal; Hao Wu (ICLR, 2025)

Lightweight Protocols for Distributed Private Quantile Estimation. - Anders Aamand; Fabrizio Boninsegna; Abigail Gentle; Jacob Imola; Rasmus Pagh (CoRR, 2025)

Differentially Private Quantiles with Smaller Error. - Jacob Imola; Fabrizio Boninsegna; Hannah Keller; Anders Aamand; Amrita Roy Chowdhury 001; Rasmus Pagh (CoRR, 2025)

Improved Approximations for Hard Graph Problems using Predictions. - Anders Aamand; Justin Y. Chen; Siddharth Gollapudi; Sandeep Silwal; Hao Wu (CoRR, 2025)

Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. - Anders Aamand; Justin Y. Chen; Mina Dalirrooyfard; Slobodan Mitrovic; Yuriy Nevmyvaka; Sandeep Silwal; Yinzhan Xu (CoRR, 2025)

On the Structure of Replicable Hypothesis Testers. - Anders Aamand; Maryam Aliakbarpour; Justin Y. Chen; Shyam Narayanan; Sandeep Silwal (CoRR, 2025)