Estimating Weighted Matchings in o(n) Space. - Elena Grigorescu; Morteza Monemizadeh; Samson Zhou (CoRR, 2016)

Longest alignment with edits in data streams. - Elena Grigorescu; Erfan Sadeqi Azer; Samson Zhou (Allerton, 2017)

Streaming Periodicity with Mismatches. - Funda Ergün; Elena Grigorescu; Erfan Sadeqi Azer; Samson Zhou (APPROX-RANDOM, 2017)

Streaming for Aibohphobes: Longest Palindrome with Mismatches. - Elena Grigorescu; Erfan Sadeqi Azer; Samson Zhou (FSTTCS, 2017)

Optimal Parametric Search for Path and Tree Partitioning. - Greg N. Frederickson; Samson Zhou (CoRR, 2017)

On the Depth-Robustness and Cumulative Pebbling Cost of Argon2i. - Jeremiah Blocki; Samson Zhou (IACR Cryptol. ePrint Arch., 2017)

Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. - Vladimir Braverman; Elena Grigorescu; Harry Lang; David P. Woodruff; Samson Zhou (APPROX-RANDOM, 2018)

On the Computational Complexity of Minimal Cumulative Cost Graph Pebbling. - Jeremiah Blocki; Samson Zhou (Financial Cryptography, 2018)

Brief Announcement: Relaxed Locally Correctable Codes in Computationally Bounded Channels. - Jeremiah Blocki; Venkata Gandikota; Elena Grigorescu; Samson Zhou (ICALP, 2018)

On the Economics of Offline Password Cracking. - Jeremiah Blocki; Benjamin Harsha; Samson Zhou (IEEE Symposium on Security and Privacy, 2018)

Bandwidth-Hard Functions: Reductions and Lower Bounds. - Jeremiah Blocki; Ling Ren 001; Samson Zhou (IACR Cryptol. ePrint Arch., 2018)

Data-Independent Memory Hard Functions: New Attacks and Stronger Constructions. - Jeremiah Blocki; Benjamin Harsha; Siteng Kang; Seunghoon Lee 004; Lu Xing; Samson Zhou (IACR Cryptol. ePrint Arch., 2018)

Approximating Properties of Data Streams - Samson Zhou (2018)

Improved Algorithms for Time Decay Streams. - Vladimir Braverman; Harry Lang; Enayat Ullah; Samson Zhou (APPROX-RANDOM, 2019)

Approximate F2-Sketching of Valuation Functions. - Grigory Yaroslavtsev; Samson Zhou (APPROX-RANDOM, 2019)

Adversarially Robust Submodular Maximization under Knapsack Constraints. - Dmitrii Avdiukhin; Slobodan Mitrovic; Grigory Yaroslavtsev; Samson Zhou (KDD, 2019)

Structural Results on Matching Estimation with Applications to Streaming. - Marc Bury; Elena Grigorescu; Andrew McGregor 001; Morteza Monemizadeh; Chris Schwiegelshohn; Sofya Vorotnikova; Samson Zhou (Algorithmica, 2019)

On Activation Function Coresets for Network Pruning. - Ben Mussay; Samson Zhou; Vladimir Braverman; Dan Feldman (CoRR, 2019)

Nearly Optimal Sparse Group Testing. - Venkata Gandikota; Elena Grigorescu; Sidharth Jaggi; Samson Zhou (IEEE Trans. Inf. Theory, 2019)

"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack. - Grigory Yaroslavtsev; Samson Zhou; Dmitrii Avdiukhin (AISTATS, 2020)

Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. - Zaoxing Liu; Samson Zhou; Ori Rottenstreich; Vladimir Braverman; Jennifer Rexford (APOCS, 2020)

Near Optimal Linear Algebra in the Online and Sliding Window Models. - Vladimir Braverman; Petros Drineas; Cameron Musco; Christopher Musco; Jalaj Upadhyay; David P. Woodruff; Samson Zhou (FOCS, 2020)

On Locally Decodable Codes in Resource Bounded Channels. - Jeremiah Blocki; Shubhang Kulkarni; Samson Zhou (ITC, 2020)

Data-Independent Neural Pruning via Coresets. - Ben Mussay; Margarita Osadchy; Vladimir Braverman; Samson Zhou; Dan Feldman (ICLR, 2020)

Computationally Data-Independent Memory Hard Functions. - Mohammad Hassan Ameri; Jeremiah Blocki; Samson Zhou (ITCS, 2020)

Approximating Cumulative Pebbling Cost Is Unique Games Hard. - Jeremiah Blocki; Seunghoon Lee 004; Samson Zhou (ITCS, 2020)

Fast Fourier Sparsity Testing. - Grigory Yaroslavtsev; Samson Zhou (SOSA, 2020)

Non-adaptive adaptive sampling on turnstile streams. - Sepideh Mahabadi; Ilya P. Razenshteyn; David P. Woodruff; Samson Zhou (STOC, 2020)

Approximation Algorithms for Sparse Principal Component Analysis. - Agniva Chowdhury; Petros Drineas; David P. Woodruff; Samson Zhou (CoRR, 2020)

Periodicity in Data Streams with Wildcards. - Funda Ergün; Elena Grigorescu; Erfan Sadeqi Azer; Samson Zhou (Theory Comput. Syst., 2020)

Efficient Coreset Constructions via Sensitivity Sampling. - Vladimir Braverman; Dan Feldman; Harry Lang; Adiel Statman; Samson Zhou (ACML, 2021)

On the Security of Proofs of Sequential Work in a Post-Quantum World. - Jeremiah Blocki; Seunghoon Lee 004; Samson Zhou (ITC, 2021)

Symmetric Norm Estimation and Regression on Sliding Windows. - Vladimir Braverman; Viska Wei; Samson Zhou (COCOON, 2021)

Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators. - David P. Woodruff; Samson Zhou (FOCS, 2021)

Separations for Estimating Large Frequency Moments on Data Streams. - David P. Woodruff; Samson Zhou (ICALP, 2021)

Learning a Latent Simplex in Input Sparsity Time. - Ainesh Bakshi; Chiranjib Bhattacharyya; Ravi Kannan; David P. Woodruff; Samson Zhou (ICLR, 2021)

Sensitivity Analysis of the Maximum Matching Problem. - Yuichi Yoshida; Samson Zhou (ITCS, 2021)

Adversarial Robustness of Streaming Algorithms through Importance Sampling. - Vladimir Braverman; Avinatan Hassidim; Yossi Matias; Mariano Schain; Sandeep Silwal; Samson Zhou (NeurIPS, 2021)

Dimensionality Reduction for Wasserstein Barycenter. - Zachary Izzo; Sandeep Silwal; Samson Zhou (NeurIPS, 2021)

Relaxed Locally Correctable Codes in Computationally Bounded Channels. - Jeremiah Blocki; Venkata Gandikota; Elena Grigorescu; Samson Zhou (IEEE Trans. Inf. Theory, 2021)

New Coresets for Projective Clustering and Applications. - Murad Tukan; Xuan Wu 002; Samson Zhou; Vladimir Braverman; Dan Feldman (AISTATS, 2022)

Adaptive Sketches for Robust Regression with Importance Sampling. - Sepideh Mahabadi; David P. Woodruff; Samson Zhou (APPROX/RANDOM, 2022)

Learning-Augmented $k$-means Clustering. - Jon C. Ergun; Zhili Feng; Sandeep Silwal; David P. Woodruff; Samson Zhou (ICLR, 2022)

Fast Regression for Structured Inputs. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (ICLR, 2022)

Hardness and Algorithms for Robust and Sparse Optimization. - Eric Price 001; Sandeep Silwal; Samson Zhou (ICML, 2022)

Noisy Boolean Hidden Matching with Applications. - Michael Kapralov; Amulya Musipatla; Jakab Tardos; David P. Woodruff; Samson Zhou (ITCS, 2022)

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming. - Elena Grigorescu; Young-San Lin; Sandeep Silwal; Maoyuan Song; Samson Zhou (NeurIPS, 2022)

The White-Box Adversarial Data Stream Model. - Miklós Ajtai; Vladimir Braverman; T. S. Jayram; Sandeep Silwal; Alec Sun; David P. Woodruff; Samson Zhou (PODS, 2022)

Truly Perfect Samplers for Data Streams and Sliding Windows. - Rajesh Jayaram; David P. Woodruff; Samson Zhou (PODS, 2022)

A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World. - Agniva Chowdhury; Aritra Bose; Samson Zhou; David P. Woodruff; Petros Drineas (RECOMB, 2022)

Memory bounds for the experts problem. - Vaidehi Srinivas; David P. Woodruff; Ziyu Xu 001; Samson Zhou (STOC, 2022)

Near-Linear Sample Complexity for Lp Polynomial Regression. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (CoRR, 2022)

Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (CoRR, 2022)

Data-Independent Structured Pruning of Neural Networks via Coresets. - Ben Mussay; Dan Feldman; Samson Zhou; Vladimir Braverman; Margarita Osadchy (IEEE Trans. Neural Networks Learn. Syst., 2022)

How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity. - Jeremiah Blocki; Elena Grigorescu; Tamalika Mukherjee; Samson Zhou (APPROX/RANDOM, 2023)

Private Data Stream Analysis for Universal Symmetric Norm Estimation. - Vladimir Braverman; Joel Manning; Zhiwei Steven Wu; Samson Zhou (APPROX/RANDOM, 2023)

Differentially Private Aggregation via Imperfect Shuffling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Jelani Nelson; Samson Zhou (ITC, 2023)

Streaming Euclidean k-median and k-means with o(log n) Space. - Vincent Cohen-Addad; David P. Woodruff; Samson Zhou (FOCS, 2023)

Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (ICLR, 2023)

Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model. - Jeremiah Blocki; Seunghoon Lee 004; Tamalika Mukherjee; Samson Zhou (ICLR, 2023)

Robust Algorithms on Adaptive Inputs from Bounded Adversaries. - Yeshwanth Cherapanamjeri; Sandeep Silwal; David P. Woodruff; Fred Zhang; Qiuyi Zhang 001; Samson Zhou (ICLR, 2023)

Provable Data Subset Selection For Efficient Neural Networks Training. - Murad Tukan; Samson Zhou; Alaa Maalouf; Daniela Rus; Vladimir Braverman; Dan Feldman (ICML, 2023)

Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. - Ameya Velingker; Maximilian Vötsch; David P. Woodruff; Samson Zhou (ICML, 2023)

Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. - Guangyao Zheng; Samson Zhou; Vladimir Braverman; Michael A. Jacobs; Vishwa Sanjay Parekh (MIDL, 2023)

Near-Optimal k-Clustering in the Sliding Window Model. - David P. Woodruff; Peilin Zhong; Samson Zhou (NeurIPS, 2023)

On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds. - David P. Woodruff; Fred Zhang; Samson Zhou (NeurIPS, 2023)

Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. - Yeshwanth Cherapanamjeri; Sandeep Silwal; David P. Woodruff; Samson Zhou (SODA, 2023)

Near-Linear Sample Complexity for Lp Polynomial Regression. - Raphael A. Meyer; Cameron Musco; Christopher Musco; David P. Woodruff; Samson Zhou (SODA, 2023)

Differentially Private L2-Heavy Hitters in the Sliding Window Model. - Jeremiah Blocki; Seunghoon Lee 004; Tamalika Mukherjee; Samson Zhou (CoRR, 2023)

Streaming Algorithms for Learning with Experts: Deterministic Versus Robust. - David P. Woodruff; Fred Zhang; Samson Zhou (CoRR, 2023)

Provable Data Subset Selection For Efficient Neural Network Training. - Murad Tukan; Samson Zhou; Alaa Maalouf; Daniela Rus; Vladimir Braverman; Dan Feldman (CoRR, 2023)

On Differential Privacy and Adaptive Data Analysis with Bounded Space. - Itai Dinur; Uri Stemmer; David P. Woodruff; Samson Zhou (IACR Cryptol. ePrint Arch., 2023)

A Strong Separation for Adversarially Robust ℓ0 Estimation for Linear Sketches. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (FOCS, 2024)

Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. - Hilal Asi; Vitaly Feldman; Jelani Nelson; Huy L. Nguyen; Kunal Talwar; Samson Zhou (ICML, 2024)

On Socially Fair Low-Rank Approximation and Column Subset Selection. - Zhao Song 002; Ali Vakilian; David P. Woodruff; Samson Zhou (NeurIPS, 2024)

Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters. - David P. Woodruff; Samson Zhou (NeurIPS, 2024)

Learning-Augmented Skip Lists. - Chunkai Fu; Jung Hoon Seo; Samson Zhou (CoRR, 2024)

A Strong Separation for Adversarially Robust ℓ0 Estimation for Linear Sketches. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (CoRR, 2024)

On Approximability of ℓ22 Min-Sum Clustering. - Karthik C. S. 001; Euiwoong Lee; Yuval Rabani; Chris Schwiegelshohn; Samson Zhou (CoRR, 2024)

Streaming Algorithms with Few State Changes. - Rajesh Jayaram; David P. Woodruff; Samson Zhou (Proc. ACM Manag. Data, 2024)

Bandwidth-Hard Functions: Reductions and Lower Bounds. - Jeremiah Blocki; Peiyuan Liu; Ling Ren 001; Samson Zhou (J. Cryptol., 2024)

On Approximability of ℓ₂² Min-Sum Clustering. - Karthik C. S. 001; Euiwoong Lee; Yuval Rabani; Chris Schwiegelshohn; Samson Zhou (SoCG, 2025)

Fair Submodular Cover. - Wenjing Chen; Shuo Xing; Samson Zhou; Victoria G. Crawford (ICLR, 2025)

Fair Clustering in the Sliding Window Model. - Vincent Cohen-Addad; Shaofeng H.-C. Jiang; Qiaoyuan Yang; Yubo Zhang; Samson Zhou (ICLR, 2025)

On the Price of Differential Privacy for Hierarchical Clustering. - Chengyuan Deng; Jie Gao 001; Jalaj Upadhyay; Chen Wang 027; Samson Zhou (ICLR, 2025)

Learning-Augmented Search Data Structures. - Chunkai Fu; Brandon G. Nguyen; Jung Hoon Seo; Ryan S. Zesch; Samson Zhou (ICLR, 2025)

Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness. - Elena Gribelyuk; Honghao Lin; David P. Woodruff; Huacheng Yu; Samson Zhou (STOC, 2025)

Fast, Space-Optimal Streaming Algorithms for Clustering and Subspace Embeddings. - Vincent Cohen-Addad; Liudeng Wang; David P. Woodruff; Samson Zhou (CoRR, 2025)

Perfect Sampling in Turnstile Streams Beyond Small Moments. - David P. Woodruff; Shenghao Xie; Samson Zhou (Proc. ACM Manag. Data, 2025)

Learning-Augmented Hierarchical Clustering. - Vladimir Braverman; Jon C. Ergun; Chen Wang 027; Samson Zhou (CoRR, 2025)

Relative Error Fair Clustering in the Weak-Strong Oracle Model. - Vladimir Braverman; Prathamesh Dharangutte; Shaofeng H.-C. Jiang; Hoai-An Nguyen; Chen Wang 027; Yubo Zhang; Samson Zhou (CoRR, 2025)

Private Training & Data Generation by Clustering Embeddings. - Felix Zhou 002; Samson Zhou; Vahab Mirrokni; Alessandro Epasto; Vincent Cohen-Addad (CoRR, 2025)

On Fine-Grained Distinct Element Estimation. - Ilias Diakonikolas; Daniel M. Kane; Jasper C. H. Lee; Thanasis Pittas; David P. Woodruff; Samson Zhou (CoRR, 2025)