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Samson Zhou

29 papers · 2020–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (10) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (5) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (71) ⚑ Prolific Year (5) πŸ’Ž Century Club (29) πŸ”₯ Unstoppable (6)

Conferences

ICLR (11) ICML (7) NIPS (7) AISTATS (2) ACML (1) MIDL (1)

Top co-authors

David Woodruff (8) Vladimir braverman (8) Sandeep Silwal (7) Dan Feldman (4) Chen Wang (3) Fred Zhang (2) Yubo Zhang (2) David P. Woodruff (2) Murad Tukan (2) Shaofeng H.-C. Jiang (2)

Keywords

approximation algorithm (5) coreset construction (5) streaming algorithm (4) robust regression (2) bicriteria approximation (2) matrix factorization (2) projective clustering (2) adversarial robustness (2) low-rank approximation (2) feature selection (1) k-means clustering (1) catastrophic forgetting (1) differential privacy (1) function approximation (1) semidefinite programming (1) online learning (1) sparse recovery (1) binary matrix factorization (1) fine-grained complexity (1) neural network training (1)

Papers

On the Price of Differential Privacy for Hierarchical Clustering ICLR 2025
Learning-Augmented Search Data Structures ICLR 2025
Fair Clustering in the Sliding Window Model ICLR 2025
On Fine-Grained Distinct Element Estimation ICML 2025
Learning-Augmented Hierarchical Clustering ICML 2025
Relative Error Fair Clustering in the Weak-Strong Oracle Model ICML 2025
Fair Submodular Cover ICLR 2025
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages ICML 2024
Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters NIPS 2024
On Socially Fair Low-Rank Approximation and Column Subset Selection NIPS 2024
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging MIDL 2023
Near-Optimal $k$-Clustering in the Sliding Window Model NIPS 2023
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds NIPS 2023
Robust Algorithms on Adaptive Inputs from Bounded Adversaries ICLR 2023
Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation ICLR 2023
Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model ICLR 2023
Provable Data Subset Selection For Efficient Neural Networks Training ICML 2023
Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization ICML 2023
New Coresets for Projective Clustering and Applications AISTATS 2022
Learning-Augmented $k$-means Clustering ICLR 2022
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming NIPS 2022
Hardness and Algorithms for Robust and Sparse Optimization ICML 2022
Fast Regression for Structured Inputs ICLR 2022
Adversarial Robustness of Streaming Algorithms through Importance Sampling NIPS 2021
Dimensionality Reduction for Wasserstein Barycenter NIPS 2021
Learning a Latent Simplex in Input Sparsity Time ICLR 2021
Efficient Coreset Constructions via Sensitivity Sampling ACML 2021
β€œBring Your Own Greedy”+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack AISTATS 2020
Data-Independent Neural Pruning via Coresets ICLR 2020