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ORCIDDavid P. Woodruff
Person information
- affiliation: Carnegie Mellon University, PA, USA
- affiliation (former): IBM Almaden Research Center, San Jose, CA, USA
- affiliation (PhD 2007): Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
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2020 – today
- 2024
[j41]Rajesh Jayaram
, David P. Woodruff, Samson Zhou:
Streaming Algorithms with Few State Changes. Proc. ACM Manag. Data 2(2): 82 (2024)- [j40]Yu Cheng, Max Li, Honghao Lin, Zi-Yi Tai, David P. Woodruff, Jason Zhang:
Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut. Proc. ACM Manag. Data 2(2): 85 (2024) - [c274]Praneeth Kacham, David P. Woodruff:
Faster Algorithms for Schatten-p Low Rank Approximation. APPROX/RANDOM 2024: 55:1-55:19 - [c273]Yi Li, Honghao Lin, David P. Woodruff:
Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms. ICLR 2024 - [c272]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh:
HyperAttention: Long-context Attention in Near-Linear Time. ICLR 2024 - [c271]Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, David P. Woodruff, Elad Hazan:
Adaptive Regret for Bandits Made Possible: Two Queries Suffice. ICLR 2024 - [c270]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. ICML 2024 - [c269]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. ICML 2024 - [c268]Ying Feng, Aayush Jain, David P. Woodruff:
Fast White-Box Adversarial Streaming Without a Random Oracle. ICML 2024 - [c267]Milind Prabhu, David P. Woodruff:
Learning Multiple Secrets in Mastermind. ICML 2024 - [c266]William J. Swartworth, David P. Woodruff:
Fast Sampling-Based Sketches for Tensors. ICML 2024 - [c265]David P. Woodruff, Taisuke Yasuda:
Reweighted Solutions for Weighted Low Rank Approximation. ICML 2024 - [c264]David P. Woodruff, Taisuke Yasuda:
Coresets for Multiple ℓp Regression. ICML 2024 - [c263]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. ITCS 2024: 13:1-13:24 - [c262]Justin Y. Chen, Piotr Indyk, David P. Woodruff:
Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions. ITCS 2024: 32:1-32:22 - [c261]Arvind V. Mahankali, David P. Woodruff, Ziyu Zhang:
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions. ITCS 2024: 79:1-79:23 - [c260]David P. Woodruff:
Approximation Algorithms on Matrices - With Some Database Applications! PODS Companion 2024: 5-6 - [c259]Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye:
Improving the Bit Complexity of Communication for Distributed Convex Optimization. STOC 2024: 1130-1140 - [c258]Mark Braverman, Sumegha Garg, Qian Li, Shuo Wang, David P. Woodruff, Jiapeng Zhang:
A New Information Complexity Measure for Multi-pass Streaming with Applications. STOC 2024: 1781-1792 - [c257]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. STOC 2024: 1911-1922 - [e2]David P. Woodruff:
Proceedings of the 2024 ACM-SIAM Symposium on Discrete Algorithms, SODA 2024, Alexandria, VA, USA, January 7-10, 2024. SIAM 2024, ISBN 978-1-61197-791-2 [contents] - [i219]Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, David P. Woodruff, Elad Hazan:
Adaptive Regret for Bandits Made Possible: Two Queries Suffice. CoRR abs/2401.09278 (2024) - [i218]Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder:
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. CoRR abs/2402.17327 (2024) - [i217]Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye:
Improving the Bit Complexity of Communication for Distributed Convex Optimization. CoRR abs/2403.19146 (2024) - [i216]Mark Braverman, Sumegha Garg, Qian Li, Shuo Wang, David P. Woodruff, Jiapeng Zhang:
A New Information Complexity Measure for Multi-pass Streaming with Applications. CoRR abs/2403.20283 (2024) - [i215]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR abs/2403.20307 (2024) - [i214]David P. Woodruff, Taisuke Yasuda:
Reweighted Solutions for Weighted Low Rank Approximation. CoRR abs/2406.02431 (2024) - [i213]David P. Woodruff, Taisuke Yasuda:
Coresets for Multiple ℓp Regression. CoRR abs/2406.02432 (2024) - [i212]Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR abs/2406.02910 (2024) - [i211]William Swartworth, David P. Woodruff:
Fast Sampling Based Sketches for Tensors. CoRR abs/2406.06735 (2024) - [i210]Ying Feng, Aayush Jain, David P. Woodruff:
Fast White-Box Adversarial Streaming Without a Random Oracle. CoRR abs/2406.06808 (2024) - [i209]Rajesh Jayaram, David P. Woodruff, Samson Zhou:
Streaming Algorithms with Few State Changes. CoRR abs/2406.06821 (2024) - [i208]Yu Cheng, Max Li, Honghao Lin, Zi-Yi Tai, David P. Woodruff, Jason Zhang:
Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut. CoRR abs/2406.13231 (2024) - [i207]Aashiq Muhamed, Oscar Li, David P. Woodruff, Mona Diab, Virginia Smith:
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. CoRR abs/2406.17660 (2024) - [i206]David P. Woodruff, Taisuke Yasuda:
Nearly Linear Sparsification of ℓp Subspace Approximation. CoRR abs/2407.03262 (2024) - [i205]Praneeth Kacham, David P. Woodruff:
Faster Algorithms for Schatten-p Low Rank Approximation. CoRR abs/2407.11959 (2024) - 2023
- [j39]David P. Woodruff:
Technical Perspective: Tapping the Link between Algorithmic Model Counting and Streaming. Commun. ACM 66(9): 94 (2023) - [j38]Rajesh Jayaram, David P. Woodruff:
Towards Optimal Moment Estimation in Streaming and Distributed Models. ACM Trans. Algorithms 19(3): 27:1-27:35 (2023) - [j37]Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang:
Recovery From Non-Decomposable Distance Oracles. IEEE Trans. Inf. Theory 69(10): 6443-6469 (2023) - [c256]Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316 - [c255]Yi Li, Honghao Lin, David P. Woodruff:
ℓp-Regression in the Arbitrary Partition Model of Communication. COLT 2023: 4902-4928 - [c254]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. EUROCRYPT (3) 2023: 35-65 - [c253]Vincent Cohen-Addad, David P. Woodruff, Samson Zhou:
Streaming Euclidean k-median and k-means with o(log n) Space. FOCS 2023: 883-908 - [c252]Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff:
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. FOCS 2023: 1515-1550 - [c251]Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff:
Learning the Positions in CountSketch. ICLR 2023 - [c250]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou:
Robust Algorithms on Adaptive Inputs from Bounded Adversaries. ICLR 2023 - [c249]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression. ICLR 2023 - [c248]Ying Feng, David P. Woodruff:
Improved Algorithms for White-Box Adversarial Streams. ICML 2023: 9962-9975 - [c247]Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou:
Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. ICML 2023: 34952-34977 - [c246]David P. Woodruff, Taisuke Yasuda:
Sharper Bounds for ℓp Sensitivity Sampling. ICML 2023: 37238-37272 - [c245]Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang:
Recovery from Non-Decomposable Distance Oracles. ITCS 2023: 73:1-73:22 - [c244]Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda:
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming. NeurIPS 2023 - [c243]Praneeth Kacham, David P. Woodruff:
Lower Bounds on Adaptive Sensing for Matrix Recovery. NeurIPS 2023 - [c242]Swati Padmanabhan, David P. Woodruff, Richard Zhang:
Computing Approximate 𝓁p Sensitivities. NeurIPS 2023 - [c241]Tamás Sarlós, Xingyou Song, David P. Woodruff, Richard Zhang:
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products. NeurIPS 2023 - [c240]David P. Woodruff, Peilin Zhong, Samson Zhou:
Near-Optimal k-Clustering in the Sliding Window Model. NeurIPS 2023 - [c239]David P. Woodruff, Fred Zhang, Samson Zhou:
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds. NeurIPS 2023 - [c238]Yi Li, Honghao Lin, David P. Woodruff:
The ℓp-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines. SODA 2023: 850-877 - [c237]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Near-Linear Sample Complexity for Lp Polynomial Regression. SODA 2023: 3959-4025 - [c236]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. SODA 2023: 4026-4049 - [c235]David P. Woodruff, Taisuke Yasuda:
Online Lewis Weight Sampling. SODA 2023: 4622-4666 - [c234]William Swartworth, David P. Woodruff:
Optimal Eigenvalue Approximation via Sketching. STOC 2023: 145-155 - [c233]David P. Woodruff, Taisuke Yasuda:
New Subset Selection Algorithms for Low Rank Approximation: Offline and Online. STOC 2023: 1802-1813 - [i204]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. CoRR abs/2302.05707 (2023) - [i203]David P. Woodruff, Fred Zhang, Samson Zhou:
Streaming Algorithms for Learning with Experts: Deterministic Versus Robust. CoRR abs/2303.01709 (2023) - [i202]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Almost Linear Constant-Factor Sketching for 𝓁1 and Logistic Regression. CoRR abs/2304.00051 (2023) - [i201]Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. CoRR abs/2304.02261 (2023) - [i200]Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff:
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. CoRR abs/2304.06853 (2023) - [i199]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou:
Robust Algorithms on Adaptive Inputs from Bounded Adversaries. CoRR abs/2304.07413 (2023) - [i198]David P. Woodruff, Taisuke Yasuda:
New Subset Selection Algorithms for Low Rank Approximation: Offline and Online. CoRR abs/2304.09217 (2023) - [i197]William Swartworth, David P. Woodruff:
Optimal Eigenvalue Approximation via Sketching. CoRR abs/2304.09281 (2023) - [i196]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. CoRR abs/2305.05826 (2023) - [i195]David P. Woodruff, Taisuke Yasuda:
Sharper Bounds for 𝓁p Sensitivity Sampling. CoRR abs/2306.00732 (2023) - [i194]Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou:
Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. CoRR abs/2306.01869 (2023) - [i193]Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff:
Learning the Positions in CountSketch. CoRR abs/2306.06611 (2023) - [i192]Ying Feng, David P. Woodruff:
Improved Algorithms for White-Box Adversarial Streams. CoRR abs/2307.03529 (2023) - [i191]Yi Li, Honghao Lin, David P. Woodruff:
𝓁p-Regression in the Arbitrary Partition Model of Communication. CoRR abs/2307.05117 (2023) - [i190]Hai Pham, Young Jin Kim, Subhabrata Mukherjee, David P. Woodruff, Barnabás Póczos, Hany Hassan Awadalla:
Task-Based MoE for Multitask Multilingual Machine Translation. CoRR abs/2308.15772 (2023) - [i189]Vincent Cohen-Addad, David P. Woodruff, Samson Zhou:
Streaming Euclidean k-median and k-means with o(log n) Space. CoRR abs/2310.02882 (2023) - [i188]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh:
HyperAttention: Long-context Attention in Near-Linear Time. CoRR abs/2310.05869 (2023) - [i187]Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda:
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming. CoRR abs/2310.19068 (2023) - [i186]David P. Woodruff, Peilin Zhong, Samson Zhou:
Near-Optimal k-Clustering in the Sliding Window Model. CoRR abs/2311.00642 (2023) - [i185]Tamás Sarlós, Xingyou Song, David P. Woodruff, Qiuyi Zhang:
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products. CoRR abs/2311.01960 (2023) - [i184]Swati Padmanabhan, David P. Woodruff, Qiuyi (Richard) Zhang:
Computing Approximate 𝓁p Sensitivities. CoRR abs/2311.04158 (2023) - [i183]Praneeth Kacham, David P. Woodruff:
Lower Bounds on Adaptive Sensing for Matrix Recovery. CoRR abs/2311.17281 (2023) - [i182]Justin Y. Chen, Piotr Indyk, David P. Woodruff:
Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions. CoRR abs/2311.17868 (2023) - [i181]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. IACR Cryptol. ePrint Arch. 2023: 171 (2023) - 2022
- [j36]Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev:
A Framework for Adversarially Robust Streaming Algorithms. J. ACM 69(2): 17:1-17:33 (2022) - [j35]David P. Woodruff:
Technical Perspective: Model Counting Meets Distinct Elements in a Data Stream. SIGMOD Rec. 51(1): 86 (2022) - [j34]Ruosong Wang, David P. Woodruff:
Tight Bounds for ℓ1 Oblivious Subspace Embeddings. ACM Trans. Algorithms 18(1): 8:1-8:32 (2022) - [c232]Yi Li, Honghao Lin, David P. Woodruff, Yuheng Zhang:
Streaming Algorithms with Large Approximation Factors. APPROX/RANDOM 2022: 13:1-13:23 - [c231]Sepideh Mahabadi, David P. Woodruff, Samson Zhou:
Adaptive Sketches for Robust Regression with Importance Sampling. APPROX/RANDOM 2022: 31:1-31:21 - [c230]Deanna Needell, William Swartworth, David P. Woodruff:
Testing Positive Semidefiniteness Using Linear Measurements. FOCS 2022: 87-97 - [c229]David P. Woodruff, Taisuke Yasuda:
High-Dimensional Geometric Streaming in Polynomial Space. FOCS 2022: 732-743 - [c228]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Linear Regression for ℓp Norms and Beyond. FOCS 2022: 744-753 - [c227]Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang:
Triangle and Four Cycle Counting with Predictions in Graph Streams. ICLR 2022 - [c226]Jon C. Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Learning-Augmented $k$-means Clustering. ICLR 2022 - [c225]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. ICLR 2022 - [c224]Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. ICML 2022: 3879-3900 - [c223]Praneeth Kacham, David P. Woodruff:
Sketching Algorithms and Lower Bounds for Ridge Regression. ICML 2022: 10539-10556 - [c222]Honghao Lin, Tian Luo, David P. Woodruff:
Learning Augmented Binary Search Trees. ICML 2022: 13431-13440 - [c221]Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff:
Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis. ICML 2022: 16083-16122 - [c220]David P. Woodruff, Amir Zandieh:
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time. ICML 2022: 23933-23964 - [c219]Anubhav Baweja, Justin Jia, David P. Woodruff:
An Efficient Semi-Streaming PTAS for Tournament Feedback Arc Set with Few Passes. ITCS 2022: 16:1-16:23 - [c218]Michael Kapralov, Amulya Musipatla, Jakab Tardos, David P. Woodruff, Samson Zhou:
Noisy Boolean Hidden Matching with Applications. ITCS 2022: 91:1-91:19 - [c217]David P. Woodruff, Fred Zhang, Richard Zhang:
Optimal Query Complexities for Dynamic Trace Estimation. NeurIPS 2022 - [c216]Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou:
The White-Box Adversarial Data Stream Model. PODS 2022: 15-27 - [c215]Rajesh Jayaram, David P. Woodruff, Samson Zhou:
Truly Perfect Samplers for Data Streams and Sliding Windows. PODS 2022: 29-40 - [c214]Agniva Chowdhury, Aritra Bose, Samson Zhou, David P. Woodruff, Petros Drineas:
A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World. RECOMB 2022: 86-106 - [c213]Piotr Indyk, Shyam Narayanan, David P. Woodruff:
Frequency Estimation with One-Sided Error. SODA 2022: 695-707 - [c212]David P. Woodruff, Taisuke Yasuda:
Improved Algorithms for Low Rank Approximation from Sparsity. SODA 2022: 2358-2403 - [c211]Nadiia Chepurko, Kenneth L. Clarkson, Praneeth Kacham, David P. Woodruff:
Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. SODA 2022: 3043-3068 - [c210]Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff:
Low-rank approximation with 1/ε1/3 matrix-vector products. STOC 2022: 1130-1143 - [c209]Vaidehi Srinivas, David P. Woodruff, Ziyu Xu, Samson Zhou:
Memory bounds for the experts problem. STOC 2022: 1158-1171 - [e1]Mikolaj Bojanczyk, Emanuela Merelli, David P. Woodruff:
49th International Colloquium on Automata, Languages, and Programming, ICALP 2022, July 4-8, 2022, Paris, France. LIPIcs 229, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2022, ISBN 978-3-95977-235-8 [contents] - [i180]David P. Woodruff, Amir Zandieh:
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time. CoRR abs/2202.04515 (2022) - [i179]Ainesh Bakshi, Kenneth L. Clarkson, David P. Woodruff:
Low-Rank Approximation with 1/ε1/3 Matrix-Vector Products. CoRR abs/2202.05120 (2022) - [i178]Yi Li, David P. Woodruff:
Tight Bounds for Sketching the Operator Norm, Schatten Norms, and Subspace Embeddings. CoRR abs/2202.09797 (2022) - [i177]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. CoRR abs/2203.07557 (2022) - [i176]Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang:
Triangle and Four Cycle Counting with Predictions in Graph Streams. CoRR abs/2203.09572 (2022) - [i175]Deanna Needell, William Swartworth, David P. Woodruff:
Testing Positive Semidefiniteness Using Linear Measurements. CoRR abs/2204.03782 (2022) - [i174]David P. Woodruff, Taisuke Yasuda:
High-Dimensional Geometric Streaming in Polynomial Space. CoRR abs/2204.03790 (2022) - [i173]Praneeth Kacham, David P. Woodruff:
Sketching Algorithms and Lower Bounds for Ridge Regression. CoRR abs/2204.06653 (2022) - [i172]Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou:
The White-Box Adversarial Data Stream Model. CoRR abs/2204.09136 (2022) - [i171]Vaidehi Srinivas, David P. Woodruff, Ziyu Xu, Samson Zhou:
Memory Bounds for the Experts Problem. CoRR abs/2204.09837 (2022) - [i170]Honghao Lin, Tian Luo, David P. Woodruff:
Learning Augmented Binary Search Trees. CoRR abs/2206.12110 (2022) - [i169]Alexander Munteanu, Simon Omlor, Zhao Song, David P. Woodruff:
Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis. CoRR abs/2206.12802 (2022) - [i168]Arvind V. Mahankali, David P. Woodruff, Ziyu Zhang:
Low Rank Approximation for General Tensor Networks. CoRR abs/2207.07417 (2022) - [i167]Sepideh Mahabadi, David P. Woodruff, Samson Zhou:
Adaptive Sketches for Robust Regression with Importance Sampling. CoRR abs/2207.07822 (2022) - [i166]Yi Li, Honghao Lin, David P. Woodruff, Yuheng Zhang:
Streaming Algorithms with Large Approximation Factors. CoRR abs/2207.08075 (2022) - [i165]David P. Woodruff, Taisuke Yasuda:
Online Lewis Weight Sampling. CoRR abs/2207.08268 (2022) - [i164]Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang:
Recovery from Non-Decomposable Distance Oracles. CoRR abs/2209.05676 (2022) - [i163]David P. Woodruff, Fred Zhang, Qiuyi Zhang:
Optimal Query Complexities for Dynamic Trace Estimation. CoRR abs/2209.15219 (2022) - [i162]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Near-Linear Sample Complexity for Lp Polynomial Regression. CoRR abs/2211.06790 (2022) - [i161]Yi Li, Honghao Lin, David P. Woodruff:
The 𝓁p-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines. CoRR abs/2211.07132 (2022) - [i160]Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. CoRR abs/2211.09964 (2022) - 2021
- [j33]David P. Woodruff, Mikolaj Bojanczyk:
ICALP 2022 - 49th EATCS International Colloquium on Automata, Languages and Programming. Bull. EATCS 135 (2021) - [j32]Rajesh Jayaram, David P. Woodruff:
Perfect Lp Sampling in a Data Stream. SIAM J. Comput. 50(2): 382-439 (2021) - [j31]Yi Li, Ruosong Wang, David P. Woodruff:
Tight Bounds for the Subspace Sketch Problem with Applications. SIAM J. Comput. 50(4): 1287-1335 (2021) - [j30]Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev:
A Framework for Adversarially Robust Streaming Algorithms. SIGMOD Rec. 50(1): 6-13 (2021) - [j29]Xiaoming Sun, David P. Woodruff, Guang Yang, Jialin Zhang:
Querying a Matrix through Matrix-Vector Products. ACM Trans. Algorithms 17(4): 31:1-31:19 (2021) - [j28]Fan Yang, Sifan Liu, Edgar Dobriban, David P. Woodruff:
How to Reduce Dimension With PCA and Random Projections? IEEE Trans. Inf. Theory 67(12): 8154-8189 (2021) - [c208]Yi Li, David P. Woodruff:
The Product of Gaussian Matrices Is Close to Gaussian. APPROX-RANDOM 2021: 35:1-35:22 - [c207]Akshay Kamath, Eric Price, David P. Woodruff:
A Simple Proof of a New Set Disjointness with Applications to Data Streams. CCC 2021: 37:1-37:24 - [c206]Praneeth Kacham, David P. Woodruff:
Reduced-Rank Regression with Operator Norm Error. COLT 2021: 2679-2716 - [c205]Yi Li, David P. Woodruff, Taisuke Yasuda:
Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing. COLT 2021: 3111-3195 - [c204]Cyrus Rashtchian, David P. Woodruff, Peng Ye, Hanlin Zhu:
Average-Case Communication Complexity of Statistical Problems. COLT 2021: 3859-3886 - [c203]David P. Woodruff, Samson Zhou:
Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators. FOCS 2021: 1183-1196 - [c202]David P. Woodruff:
A Very Sketchy Talk (Invited Talk). ICALP 2021: 6:1-6:8 - [c201]David P. Woodruff, Samson Zhou:
Separations for Estimating Large Frequency Moments on Data Streams. ICALP 2021: 112:1-112:21 - [c200]Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input Sparsity Time. ICLR 2021 - [c199]Zhili Feng, Praneeth Kacham, David P. Woodruff:
Dimensionality Reduction for the Sum-of-Distances Metric. ICML 2021: 3220-3229 - [c198]Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye:
In-Database Regression in Input Sparsity Time. ICML 2021: 4797-4806 - [c197]Shuli Jiang, Dennis Li, Irene Mengze Li, Arvind V. Mahankali, David P. Woodruff:
Streaming and Distributed Algorithms for Robust Column Subset Selection. ICML 2021: 4971-4981 - [c196]Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David P. Woodruff:
Single Pass Entrywise-Transformed Low Rank Approximation. ICML 2021: 4982-4991 - [c195]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Oblivious Sketching for Logistic Regression. ICML 2021: 7861-7871 - [c194]Zhao Song, David P. Woodruff, Zheng Yu, Lichen Zhang:
Fast Sketching of Polynomial Kernels of Polynomial Degree. ICML 2021: 9812-9823 - [c193]Cameron Musco, Christopher Musco, David P. Woodruff:
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation. ITCS 2021: 6:1-6:20 - [c192]Piotr Indyk, Tal Wagner, David P. Woodruff:
Few-Shot Data-Driven Algorithms for Low Rank Approximation. NeurIPS 2021: 10678-10690 - [c191]Arvind V. Mahankali, David P. Woodruff:
Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters. NeurIPS 2021: 14407-14420 - [c190]Shuli Jiang, Hai Pham, David P. Woodruff, Qiuyi (Richard) Zhang:
Optimal Sketching for Trace Estimation. NeurIPS 2021: 23741-23753 - [c189]Graham Cormode, Charlie Dickens, David P. Woodruff:
Subspace Exploration: Bounds on Projected Frequency Estimation. PODS 2021: 273-284 - [c188]Arvind V. Mahankali, David P. Woodruff:
Optimal ℓ1 Column Subset Selection and a Fast PTAS for Low Rank Approximation. SODA 2021: 560-578 - [c187]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. SOSA 2021: 142-155 - [c186]Zhili Feng, Fred Roosta, David P. Woodruff:
Non-PSD matrix sketching with applications to regression and optimization. UAI 2021: 1841-1851 - [i159]Graham Cormode, Charlie Dickens, David P. Woodruff:
Subspace exploration: Bounds on Projected Frequency Estimation. CoRR abs/2101.07546 (2021) - [i158]Yi Li, Honghao Lin, David P. Woodruff:
Learning-Augmented Sketches for Hessians. CoRR abs/2102.12317 (2021) - [i157]Yi Li, David P. Woodruff, Taisuke Yasuda:
Exponentially Improved Dimensionality Reduction for 𝓁1: Subspace Embeddings and Independence Testing. CoRR abs/2104.12946 (2021) - [i156]David P. Woodruff, Samson Zhou:
Separations for Estimating Large Frequency Moments on Data Streams. CoRR abs/2105.03773 (2021) - [i155]Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input-Sparsity Time. CoRR abs/2105.08005 (2021) - [i154]Akshay Kamath, Eric Price, David P. Woodruff:
A Simple Proof of a New Set Disjointness with Applications to Data Streams. CoRR abs/2105.11338 (2021) - [i153]Zhili Feng, Fred Roosta, David P. Woodruff:
Non-PSD Matrix Sketching with Applications to Regression and Optimization. CoRR abs/2106.08544 (2021) - [i152]Cyrus Rashtchian, David P. Woodruff, Peng Ye, Hanlin Zhu:
Average-Case Communication Complexity of Statistical Problems. CoRR abs/2107.01335 (2021) - [i151]Michael Kapralov, Amulya Musipatla, Jakab Tardos, David P. Woodruff, Samson Zhou:
Noisy Boolean Hidden Matching with Applications. CoRR abs/2107.02578 (2021) - [i150]Shuchi Chawla, Jelani Nelson, Chris Umans, David P. Woodruff:
Visions in Theoretical Computer Science: A Report on the TCS Visioning Workshop 2020. CoRR abs/2107.02846 (2021) - [i149]Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye:
In-Database Regression in Input Sparsity Time. CoRR abs/2107.05672 (2021) - [i148]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Oblivious sketching for logistic regression. CoRR abs/2107.06615 (2021) - [i147]Anubhav Baweja, Justin Jia, David P. Woodruff:
An Efficient Semi-Streaming PTAS for Tournament Feedback ArcSet with Few Passes. CoRR abs/2107.07141 (2021) - [i146]Shuli Jiang, Dongyu Li, Irene Mengze Li, Arvind V. Mahankali, David P. Woodruff:
Streaming and Distributed Algorithms for Robust Column Subset Selection. CoRR abs/2107.07657 (2021) - [i145]Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David P. Woodruff:
Single Pass Entrywise-Transformed Low Rank Approximation. CoRR abs/2107.07889 (2021) - [i144]Nadiia Chepurko, Kenneth L. Clarkson, Praneeth Kacham, David P. Woodruff:
Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. CoRR abs/2107.08090 (2021) - [i143]Zhao Song, David P. Woodruff, Zheng Yu, Lichen Zhang:
Fast Sketching of Polynomial Kernels of Polynomial Degree. CoRR abs/2108.09420 (2021) - [i142]Yi Li, David P. Woodruff:
The Product of Gaussian Matrices is Close to Gaussian. CoRR abs/2108.09887 (2021) - [i141]Rajesh Jayaram, David P. Woodruff, Samson Zhou:
Truly Perfect Samplers for Data Streams and Sliding Windows. CoRR abs/2108.12017 (2021) - [i140]Jon Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Learning-Augmented k-means Clustering. CoRR abs/2110.14094 (2021) - [i139]Shuli Jiang, Hai Pham, David P. Woodruff, Qiuyi (Richard) Zhang:
Optimal Sketching for Trace Estimation. CoRR abs/2111.00664 (2021) - [i138]David P. Woodruff, Taisuke Yasuda:
Improved Algorithms for Low Rank Approximation from Sparsity. CoRR abs/2111.00668 (2021) - [i137]Piotr Indyk, Shyam Narayanan, David P. Woodruff:
Frequency Estimation with One-Sided Error. CoRR abs/2111.03953 (2021) - [i136]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Sampling for Linear Regression Beyond the $\ell_2$ Norm. CoRR abs/2111.04888 (2021) - 2020
- [j27]Yin Tat Lee, Marcin Pilipczuk, David P. Woodruff:
Introduction to the Special Issue on SODA'18. ACM Trans. Algorithms 16(1): 1:1-1:2 (2020) - [c185]Praneeth Kacham, David P. Woodruff:
Optimal Deterministic Coresets for Ridge Regression. AISTATS 2020: 4141-4150 - [c184]Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff:
Automatic Differentiation of Sketched Regression. AISTATS 2020: 4367-4376 - [c183]Cyrus Rashtchian, David P. Woodruff, Hanlin Zhu:
Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems. APPROX-RANDOM 2020: 26:1-26:20 - [c182]Alexandr Andoni, Collin Burns, Yi Li, Sepideh Mahabadi, David P. Woodruff:
Streaming Complexity of SVMs. APPROX-RANDOM 2020: 50:1-50:22 - [c181]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. APPROX-RANDOM 2020: 64:1-64:22 - [c180]Mark Braverman, Sumegha Garg, David P. Woodruff:
The Coin Problem with Applications to Data Streams. FOCS 2020: 318-329 - [c179]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Robust and Sample Optimal Algorithms for PSD Low Rank Approximation. FOCS 2020: 506-516 - [c178]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. FOCS 2020: 517-528 - [c177]Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang:
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. ICLR 2020 - [c176]Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff:
Learning-Augmented Data Stream Algorithms. ICLR 2020 - [c175]Yi Li, David P. Woodruff:
Input-Sparsity Low Rank Approximation in Schatten Norm. ICML 2020: 6001-6009 - [c174]David P. Woodruff, Amir Zandieh:
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling. ICML 2020: 10324-10333 - [c173]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
Graph Spanners in the Message-Passing Model. ITCS 2020: 77:1-77:18 - [c172]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-Deterministic Streaming. ITCS 2020: 79:1-79:25 - [c171]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for ℓp-Sampling Without Replacement. NeurIPS 2020 - [c170]Quang Minh Hoang, Nghia Hoang, Hai Pham, David P. Woodruff:
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. NeurIPS 2020 - [c169]Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev:
A Framework for Adversarially Robust Streaming Algorithms. PODS 2020: 63-80 - [c168]Debmalya Mandal, Nisarg Shah, David P. Woodruff:
Optimal Communication-Distortion Tradeoff in Voting. EC 2020: 795-813 - [c167]Thomas D. Ahle, Michael Kapralov, Jakob Bæk Tejs Knudsen, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh:
Oblivious Sketching of High-Degree Polynomial Kernels. SODA 2020: 141-160 - [c166]Yi Li, Ruosong Wang, David P. Woodruff:
Tight Bounds for the Subspace Sketch Problem with Applications. SODA 2020: 1655-1674 - [c165]Santosh S. Vempala, Ruosong Wang, David P. Woodruff:
The Communication Complexity of Optimization. SODA 2020: 1733-1752 - [c164]Sepideh Mahabadi, Ilya P. Razenshteyn, David P. Woodruff, Samson Zhou:
Non-adaptive adaptive sampling on turnstile streams. STOC 2020: 1251-1264 - [c163]Cyrus Rashtchian, Aneesh Sharma, David P. Woodruff:
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew. WWW 2020: 2998-3004 - [i135]Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang:
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. CoRR abs/2002.08202 (2020) - [i134]Cyrus Rashtchian, Aneesh Sharma, David P. Woodruff:
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew. CoRR abs/2003.02972 (2020) - [i133]Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev:
A Framework for Adversarially Robust Streaming Algorithms. CoRR abs/2003.14265 (2020) - [i132]Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 𝓁1-Norm Loss. CoRR abs/2004.07986 (2020) - [i131]Sepideh Mahabadi, Ilya P. Razenshteyn, David P. Woodruff, Samson Zhou:
Non-Adaptive Adaptive Sampling on Turnstile Streams. CoRR abs/2004.10969 (2020) - [i130]Yi Li, David P. Woodruff:
Input-Sparsity Low Rank Approximation in Schatten Norm. CoRR abs/2004.12646 (2020) - [i129]Agniva Chowdhury, Petros Drineas, David P. Woodruff, Samson Zhou:
Approximation Algorithms for Sparse Principal Component Analysis. CoRR abs/2006.12748 (2020) - [i128]Cyrus Rashtchian, David P. Woodruff, Hanlin Zhu:
Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems. CoRR abs/2006.14015 (2020) - [i127]Alexandr Andoni, Collin Burns, Yi Li, Sepideh Mahabadi, David P. Woodruff:
Streaming Complexity of SVMs. CoRR abs/2007.03633 (2020) - [i126]David P. Woodruff, Amir Zandieh:
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling. CoRR abs/2007.03927 (2020) - [i125]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for 𝓁p-Sampling Without Replacement. CoRR abs/2007.06744 (2020) - [i124]Simin Liu, Tianrui Liu, Ali Vakilian, Yulin Wan, David P. Woodruff:
On Learned Sketches for Randomized Numerical Linear Algebra. CoRR abs/2007.09890 (2020) - [i123]Arvind V. Mahankali, David P. Woodruff:
Optimal 𝓁1 Column Subset Selection and a Fast PTAS for Low Rank Approximation. CoRR abs/2007.10307 (2020) - [i122]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. CoRR abs/2010.09649 (2020) - [i121]Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. CoRR abs/2011.04125 (2020) - [i120]Praneeth Kacham, David P. Woodruff:
Reduced-Rank Regression with Operator Norm Error. CoRR abs/2011.04564 (2020) - [i119]David P. Woodruff, Samson Zhou:
Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators. CoRR abs/2011.07471 (2020) - [i118]Quang Minh Hoang, Trong Nghia Hoang, Hai Pham, David P. Woodruff:
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes. CoRR abs/2011.08432 (2020) - [i117]Mark Braverman, Sumegha Garg, David P. Woodruff:
The Coin Problem with Applications to Data Streams. Electron. Colloquium Comput. Complex. TR20 (2020)
2010 – 2019
- 2019
- [j26]Maria-Florina Balcan, Yingyu Liang, Zhao Song, David P. Woodruff, Hongyang Zhang:
Non-Convex Matrix Completion and Related Problems via Strong Duality. J. Mach. Learn. Res. 20: 102:1-102:56 (2019) - [j25]Yi Li, Huy L. Nguyen, David P. Woodruff:
On Approximating Matrix Norms in Data Streams. SIAM J. Comput. 48(6): 1643-1697 (2019) - [j24]Arnab Bhattacharyya, Palash Dey, David P. Woodruff:
An Optimal Algorithm for ℓ1-Heavy Hitters in Insertion Streams and Related Problems. ACM Trans. Algorithms 15(1): 2:1-2:27 (2019) - [c162]Xiaofei Shi, David P. Woodruff:
Sublinear Time Numerical Linear Algebra for Structured Matrices. AAAI 2019: 4918-4925 - [c161]John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse $L_p$-norm Regression With Optimal Probability. AISTATS 2019: 1042-1050 - [c160]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
The Query Complexity of Mastermind with lp Distances. APPROX-RANDOM 2019: 1:1-1:11 - [c159]Rajesh Jayaram, David P. Woodruff:
Towards Optimal Moment Estimation in Streaming and Distributed Models. APPROX-RANDOM 2019: 29:1-29:21 - [c158]Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff:
Learning Two Layer Rectified Neural Networks in Polynomial Time. COLT 2019: 195-268 - [c157]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. COLT 2019: 727-757 - [c156]Piotr Indyk, Ali Vakilian, Tal Wagner, David P. Woodruff:
Sample-Optimal Low-Rank Approximation of Distance Matrices. COLT 2019: 1723-1751 - [c155]Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang:
The One-Way Communication Complexity of Dynamic Time Warping Distance. SoCG 2019: 16:1-16:15 - [c154]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. GI-Jahrestagung 2019: 267-268 - [c153]Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David P. Woodruff:
Robust Communication-Optimal Distributed Clustering Algorithms. ICALP 2019: 18:1-18:16 - [c152]Xiaoming Sun, David P. Woodruff, Guang Yang, Jialin Zhang:
Querying a Matrix Through Matrix-Vector Products. ICALP 2019: 94:1-94:16 - [c151]David P. Woodruff, Guang Yang:
Separating k-Player from t-Player One-Way Communication, with Applications to Data Streams. ICALP 2019: 97:1-97:14 - [c150]Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff:
Dimensionality Reduction for Tukey Regression. ICML 2019: 1262-1271 - [c149]Ravi Kumar, Rina Panigrahy, Ali Rahimi, David P. Woodruff:
Faster Algorithms for Binary Matrix Factorization. ICML 2019: 3551-3559 - [c148]Taisuke Yasuda, David P. Woodruff, Manuel Fernandez:
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering. ICML 2019: 7055-7063 - [c147]Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang:
Total Least Squares Regression in Input Sparsity Time. NeurIPS 2019: 2478-2489 - [c146]Frank Ban, David P. Woodruff, Qiuyi (Richard) Zhang:
Regularized Weighted Low Rank Approximation. NeurIPS 2019: 4061-4071 - [c145]Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. NeurIPS 2019: 4739-4750 - [c144]Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Column Subset Selection. NeurIPS 2019: 6120-6131 - [c143]Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff:
Efficient and Thrifty Voting by Any Means Necessary. NeurIPS 2019: 7178-7189 - [c142]Michela Meister, Tamás Sarlós, David P. Woodruff:
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels. NeurIPS 2019: 9470-9481 - [c141]Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 퓁1-Norm Loss. NeurIPS 2019: 10111-10121 - [c140]Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, David P. Woodruff:
Weighted Reservoir Sampling from Distributed Streams. PODS 2019: 218-235 - [c139]Can Kockan, Kaiyuan Zhu, Natnatee Dokmai, Nikolai Karpov, M. Oguzhan Külekci, David P. Woodruff, Süleyman Cenk Sahinalp:
Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave. RECOMB 2019: 302-304 - [c138]Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang:
Testing Matrix Rank, Optimally. SODA 2019: 727-746 - [c137]Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, Euiwoong Lee, David P. Woodruff:
A PTAS for ℓp-Low Rank Approximation. SODA 2019: 747-766 - [c136]Ruosong Wang, David P. Woodruff:
Tight Bounds for ℓp Oblivious Subspace Embeddings. SODA 2019: 1825-1843 - [c135]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. SODA 2019: 2772-2789 - [i116]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. CoRR abs/1902.10328 (2019) - [i115]Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang:
The One-Way Communication Complexity of Dynamic Time Warping Distance. CoRR abs/1903.03520 (2019) - [i114]Srikanta Tirthapura, David P. Woodruff:
Optimal Random Sampling from Distributed Streams Revisited. CoRR abs/1903.12065 (2019) - [i113]Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, David P. Woodruff:
Weighted Reservoir Sampling from Distributed Streams. CoRR abs/1904.04126 (2019) - [i112]Yi Li, Ruosong Wang, David P. Woodruff:
Tight Bounds for the Subspace Sketch Problem with Applications. CoRR abs/1904.05543 (2019) - [i111]Cameron Musco, Christopher Musco, David P. Woodruff:
Low-Rank Approximation from Communication Complexity. CoRR abs/1904.09841 (2019) - [i110]Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff:
Dimensionality Reduction for Tukey Regression. CoRR abs/1905.05376 (2019) - [i109]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k-means Clustering. CoRR abs/1905.06394 (2019) - [i108]David P. Woodruff, Guang Yang:
Separating k-Player from t-Player One-Way Communication, with Applications to Data Streams. CoRR abs/1905.07135 (2019) - [i107]Piotr Indyk, Ali Vakilian, Tal Wagner, David P. Woodruff:
Sample-Optimal Low-Rank Approximation of Distance Matrices. CoRR abs/1906.00339 (2019) - [i106]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR abs/1906.04661 (2019) - [i105]Xiaoming Sun, David P. Woodruff, Guang Yang, Jialin Zhang:
Querying a Matrix through Matrix-Vector Products. CoRR abs/1906.05736 (2019) - [i104]Santosh S. Vempala, Ruosong Wang, David P. Woodruff:
The Communication Complexity of Optimization. CoRR abs/1906.05832 (2019) - [i103]Rajesh Jayaram, David P. Woodruff:
Towards Optimal Moment Estimation in Streaming and Distributed Models. CoRR abs/1907.05816 (2019) - [i102]Michael Kapralov, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh:
Oblivious Sketching of High-Degree Polynomial Kernels. CoRR abs/1909.01410 (2019) - [i101]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
The Query Complexity of Mastermind with 𝓁p Distances. CoRR abs/1909.10668 (2019) - [i100]Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang:
Total Least Squares Regression in Input Sparsity Time. CoRR abs/1909.12441 (2019) - [i99]Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. CoRR abs/1909.13384 (2019) - [i98]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
Graph Spanners in the Message-Passing Model. CoRR abs/1911.05991 (2019) - [i97]Frank Ban, David P. Woodruff, Qiuyi (Richard) Zhang:
Regularized Weighted Low Rank Approximation. CoRR abs/1911.06958 (2019) - [i96]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-deterministic Streaming. CoRR abs/1911.11368 (2019) - [i95]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation. CoRR abs/1912.04177 (2019) - [i94]Xiaofei Shi, David P. Woodruff:
Sublinear Time Numerical Linear Algebra for Structured Matrices. CoRR abs/1912.06060 (2019) - [i93]Zhili Feng, Praneeth Kacham, David P. Woodruff:
Strong Coresets for Subspace Approximation and k-Median in Nearly Linear Time. CoRR abs/1912.12003 (2019) - [i92]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-deterministic Streaming. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [c134]Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff:
Sketching for Kronecker Product Regression and P-splines. AISTATS 2018: 1299-1308 - [c133]Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou:
Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. APPROX-RANDOM 2018: 7:1-7:22 - [c132]Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff:
On Sketching the q to p Norms. APPROX-RANDOM 2018: 15:1-15:20 - [c131]Yi Li, Vasileios Nakos, David P. Woodruff:
On Low-Risk Heavy Hitters and Sparse Recovery Schemes. APPROX-RANDOM 2018: 19:1-19:13 - [c130]Rajesh Jayaram, David P. Woodruff:
Perfect Lp Sampling in a Data Stream. FOCS 2018: 544-555 - [c129]Christian Sohler, David P. Woodruff:
Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimension. FOCS 2018: 802-813 - [c128]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. ICALP 2018: 25:1-25:14 - [c127]Sumit Ganguly, David P. Woodruff:
High Probability Frequency Moment Sketches. ICALP 2018: 58:1-58:15 - [c126]Vasileios Nakos, Xiaofei Shi, David P. Woodruff, Hongyang Zhang:
Improved Algorithms for Adaptive Compressed Sensing. ICALP 2018: 90:1-90:14 - [c125]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang:
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. ICML 2018: 648-657 - [c124]Graham Cormode, Charlie Dickens, David P. Woodruff:
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms. ICML 2018: 1048-1056 - [c123]Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang:
Matrix Completion and Related Problems via Strong Duality. ITCS 2018: 5:1-5:22 - [c122]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS 2018: 8:1-8:21 - [c121]Yogesh Dahiya, Dimitris Konomis, David P. Woodruff:
An Empirical Evaluation of Sketching for Numerical Linear Algebra. KDD 2018: 1292-1300 - [c120]Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. NeurIPS 2018: 3786-3796 - [c119]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. NeurIPS 2018: 6562-6571 - [c118]Roie Levin, Anish Prasad Sevekari, David P. Woodruff:
Robust Subspace Approximation in a Stream. NeurIPS 2018: 10706-10716 - [c117]Rajesh Jayaram, David P. Woodruff:
Data Streams with Bounded Deletions. PODS 2018: 341-354 - [c116]David P. Woodruff, Qin Zhang:
Distributed Statistical Estimation of Matrix Products with Applications. PODS 2018: 383-394 - [r2]David P. Woodruff:
Frequency Moments. Encyclopedia of Database Systems (2nd ed.) 2018 - [i91]Ruosong Wang, David P. Woodruff:
Tight Bounds for 𝓁p Oblivious Subspace Embeddings. CoRR abs/1801.04414 (2018) - [i90]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. CoRR abs/1803.02270 (2018) - [i89]Rajesh Jayaram, David P. Woodruff:
Data Streams with Bounded Deletions. CoRR abs/1803.08777 (2018) - [i88]Vasileios Nakos, Xiaofei Shi, David P. Woodruff, Hongyang Zhang:
Improved Algorithms for Adaptive Compressed Sensing. CoRR abs/1804.09673 (2018) - [i87]Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou:
Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. CoRR abs/1805.00212 (2018) - [i86]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. CoRR abs/1805.03765 (2018) - [i85]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. CoRR abs/1805.08571 (2018) - [i84]Sumit Ganguly, David P. Woodruff:
High Probability Frequency Moment Sketches. CoRR abs/1805.10885 (2018) - [i83]Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff:
On Sketching the q to p norms. CoRR abs/1806.06429 (2018) - [i82]John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse 𝓁p-norm Regression With Optimal Probability. CoRR abs/1806.10222 (2018) - [i81]David P. Woodruff, Qin Zhang:
Distributed Statistical Estimation of Matrix Products with Applications. CoRR abs/1807.00878 (2018) - [i80]Graham Cormode, Charlie Dickens, David P. Woodruff:
Leveraging Well-Conditioned Bases: Streaming \& Distributed Summaries in Minkowski p-Norms. CoRR abs/1807.02571 (2018) - [i79]Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, Euiwoong Lee, David P. Woodruff:
A PTAS for 𝓁p-Low Rank Approximation. CoRR abs/1807.06101 (2018) - [i78]Rajesh Jayaram, David P. Woodruff:
Perfect Lp Sampling in a Data Stream. CoRR abs/1808.05497 (2018) - [i77]Christian Sohler, David P. Woodruff:
Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimension. CoRR abs/1809.02961 (2018) - [i76]Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. CoRR abs/1809.06986 (2018) - [i75]Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang:
Testing Matrix Rank, Optimally. CoRR abs/1810.08171 (2018) - [i74]Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Entrywise Low Rank Approximation. CoRR abs/1811.01442 (2018) - [i73]Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff:
Learning Two Layer Rectified Neural Networks in Polynomial Time. CoRR abs/1811.01885 (2018) - [i72]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [j23]David P. Woodruff, Qin Zhang:
When distributed computation is communication expensive. Distributed Comput. 30(5): 309-323 (2017) - [j22]Christos Boutsidis, David P. Woodruff:
Optimal CUR Matrix Decompositions. SIAM J. Comput. 46(2): 543-589 (2017) - [j21]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Faster Kernel Ridge Regression Using Sketching and Preconditioning. SIAM J. Matrix Anal. Appl. 38(4): 1116-1138 (2017) - [c115]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Sharper Bounds for Regularized Data Fitting. APPROX-RANDOM 2017: 27:1-27:22 - [c114]David P. Woodruff:
Sketching for Geometric Problems (Invited Talk). ESA 2017: 1:1-1:5 - [c113]Michael Kapralov, Jelani Nelson, Jakub Pachocki, Zhengyu Wang, David P. Woodruff, Mobin Yahyazadeh:
Optimal Lower Bounds for Universal Relation, and for Samplers and Finding Duplicates in Streams. FOCS 2017: 475-486 - [c112]Cameron Musco, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. FOCS 2017: 672-683 - [c111]Eric Price, Zhao Song, David P. Woodruff:
Fast Regression with an $ell_infty$ Guarantee. ICALP 2017: 59:1-59:14 - [c110]Yi Li, David P. Woodruff:
Embeddings of Schatten Norms with Applications to Data Streams. ICALP 2017: 60:1-60:14 - [c109]Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff:
Algorithms for $\ell_p$ Low-Rank Approximation. ICML 2017: 806-814 - [c108]Xingguo Li, Jarvis D. Haupt, David P. Woodruff:
Near Optimal Sketching of Low-Rank Tensor Regression. NIPS 2017: 3466-3476 - [c107]Cameron Musco, David P. Woodruff:
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? NIPS 2017: 4435-4445 - [c106]Karl Bringmann, Pavel Kolev, David P. Woodruff:
Approximation Algorithms for l0-Low Rank Approximation. NIPS 2017: 6648-6659 - [c105]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff:
BPTree: An ℓ2 Heavy Hitters Algorithm Using Constant Memory. PODS 2017: 361-376 - [c104]Santosh S. Vempala, David P. Woodruff:
Adaptive Matrix Vector Product. SODA 2017: 2044-2060 - [c103]Kenneth L. Clarkson, David P. Woodruff:
Low-Rank PSD Approximation in Input-Sparsity Time. SODA 2017: 2061-2072 - [c102]Zhao Song, David P. Woodruff, Peilin Zhong:
Low rank approximation with entrywise l1-norm error. STOC 2017: 688-701 - [i71]Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang:
Communication-Optimal Distributed Clustering. CoRR abs/1702.00196 (2017) - [i70]Yi Li, David P. Woodruff:
Embeddings of Schatten Norms with Applications to Data Streams. CoRR abs/1702.05626 (2017) - [i69]Michael Kapralov, Jelani Nelson, Jakub Pachocki, Zhengyu Wang, David P. Woodruff, Mobin Yahyazadeh:
Optimal lower bounds for universal relation, and for samplers and finding duplicates in streams. CoRR abs/1704.00633 (2017) - [i68]Cameron Musco, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. CoRR abs/1704.03371 (2017) - [i67]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR abs/1704.04163 (2017) - [i66]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. CoRR abs/1704.08246 (2017) - [i65]Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, Hongyang Zhang:
Optimal Sample Complexity for Matrix Completion and Related Problems via 𝓁s2-Regularization. CoRR abs/1704.08683 (2017) - [i64]Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff:
Algorithms for $\ell_p$ Low Rank Approximation. CoRR abs/1705.06730 (2017) - [i63]Eric Price, Zhao Song, David P. Woodruff:
Fast Regression with an $\ell_\infty$ Guarantee. CoRR abs/1705.10723 (2017) - [i62]Yi Li, Vasileios Nakos, David P. Woodruff:
On Low-Risk Heavy Hitters and Sparse Recovery Schemes. CoRR abs/1709.02919 (2017) - [i61]Jarvis D. Haupt, Xingguo Li, David P. Woodruff:
Near Optimal Sketching of Low-Rank Tensor Regression. CoRR abs/1709.07093 (2017) - [i60]Karl Bringmann, Pavel Kolev, David P. Woodruff:
Approximation Algorithms for ࡁ0-Low Rank Approximation. CoRR abs/1710.11253 (2017) - [i59]Cameron Musco, David P. Woodruff:
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? CoRR abs/1711.01596 (2017) - [i58]Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff:
Sketching for Kronecker Product Regression and P-splines. CoRR abs/1712.09473 (2017) - [i57]Martin Dietzfelbinger, Michael Mitzenmacher, Rasmus Pagh, David P. Woodruff, Martin Aumüller:
Theory and Applications of Hashing (Dagstuhl Seminar 17181). Dagstuhl Reports 7(5): 1-21 (2017) - [i56]Vladimir Braverman, David P. Woodruff, Ke Yi:
Processing Big Data Streams (NII Shonan Meeting 2017-7). NII Shonan Meet. Rep. 2017 (2017) - 2016
- [j20]Andrew McGregor, A. Pavan, Srikanta Tirthapura, David P. Woodruff:
Space-Efficient Estimation of Statistics Over Sub-Sampled Streams. Algorithmica 74(2): 787-811 (2016) - [j19]Mark Braverman, David P. Woodruff:
Guest Editorial for Information Complexity and Applications. Algorithmica 76(3): 595-596 (2016) - [j18]Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, David P. Woodruff, Grigory Yaroslavtsev:
Certifying Equality With Limited Interaction. Algorithmica 76(3): 796-845 (2016) - [j17]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform and Faster Robust Linear Regression. SIAM J. Comput. 45(3): 763-810 (2016) - [j16]Mina Ghashami, Edo Liberty, Jeff M. Phillips, David P. Woodruff:
Frequent Directions: Simple and Deterministic Matrix Sketching. SIAM J. Comput. 45(5): 1762-1792 (2016) - [j15]Yuval Rabani, Andréa W. Richa, Jared Saia, David P. Woodruff:
Editorial to the Special Issue on SODA'12. ACM Trans. Algorithms 12(1): 1:1 (2016) - [j14]Yung-Yu Chung, Srikanta Tirthapura, David P. Woodruff:
A Simple Message-Optimal Algorithm for Random Sampling from a Distributed Stream. IEEE Trans. Knowl. Data Eng. 28(6): 1356-1368 (2016) - [c101]Yi Li, David P. Woodruff:
Tight Bounds for Sketching the Operator Norm, Schatten Norms, and Subspace Embeddings. APPROX-RANDOM 2016: 39:1-39:11 - [c100]Yuqing Ai, Wei Hu, Yi Li, David P. Woodruff:
New Characterizations in Turnstile Streams with Applications. CCC 2016: 20:1-20:22 - [c99]Michael S. Crouch, Andrew McGregor, Gregory Valiant, David P. Woodruff:
Stochastic Streams: Sample Complexity vs. Space Complexity. ESA 2016: 32:1-32:15 - [c98]Michael B. Cohen, Jelani Nelson, David P. Woodruff:
Optimal Approximate Matrix Product in Terms of Stable Rank. ICALP 2016: 11:1-11:14 - [c97]David P. Woodruff, Peilin Zhong:
Distributed low rank approximation of implicit functions of a matrix. ICDE 2016: 847-858 - [c96]David P. Woodruff:
New Algorithms for Heavy Hitters in Data Streams (Invited Talk). ICDT 2016: 4:1-4:12 - [c95]Michael Kapralov, Vamsi K. Potluru, David P. Woodruff:
How to Fake Multiply by a Gaussian Matrix. ICML 2016: 2101-2110 - [c94]Alexandr Andoni, Jiecao Chen, Robert Krauthgamer, Bo Qin, David P. Woodruff, Qin Zhang:
On Sketching Quadratic Forms. ITCS 2016: 311-319 - [c93]Maria-Florina Balcan, Yingyu Liang, Le Song, David P. Woodruff, Bo Xie:
Communication Efficient Distributed Kernel Principal Component Analysis. KDD 2016: 725-734 - [c92]Zhao Song, David P. Woodruff, Huan Zhang:
Sublinear Time Orthogonal Tensor Decomposition. NIPS 2016: 793-801 - [c91]Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang:
Communication-Optimal Distributed Clustering. NIPS 2016: 3720-3728 - [c90]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. PODS 2016: 261-276 - [c89]Arnab Bhattacharyya, Palash Dey, David P. Woodruff:
An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems. PODS 2016: 385-400 - [c88]Arturs Backurs, Piotr Indyk, Ilya P. Razenshteyn, David P. Woodruff:
Nearly-optimal bounds for sparse recovery in generic norms, with applications to k-median sketching. SODA 2016: 318-337 - [c87]Hossein Esfandiari, MohammadTaghi Hajiaghayi, David P. Woodruff:
Brief Announcement: Applications of Uniform Sampling: Densest Subgraph and Beyond. SPAA 2016: 397-399 - [c86]Christos Boutsidis, David P. Woodruff, Peilin Zhong:
Optimal principal component analysis in distributed and streaming models. STOC 2016: 236-249 - [c85]Ilya P. Razenshteyn, Zhao Song, David P. Woodruff:
Weighted low rank approximations with provable guarantees. STOC 2016: 250-263 - [c84]Yi Li, David P. Woodruff:
On approximating functions of the singular values in a stream. STOC 2016: 726-739 - [c83]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff:
Beating CountSketch for heavy hitters in insertion streams. STOC 2016: 740-753 - [c82]Mark Braverman, Ankit Garg, Tengyu Ma, Huy L. Nguyen, David P. Woodruff:
Communication lower bounds for statistical estimation problems via a distributed data processing inequality. STOC 2016: 1011-1020 - [i55]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. CoRR abs/1601.07473 (2016) - [i54]David P. Woodruff, Peilin Zhong:
Distributed Low Rank Approximation of Implicit Functions of a Matrix. CoRR abs/1601.07721 (2016) - [i53]Arnab Bhattacharyya, Palash Dey, David P. Woodruff:
An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems. CoRR abs/1603.00213 (2016) - [i52]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff:
BPTree: an ℓ2 heavy hitters algorithm using constant memory. CoRR abs/1603.00759 (2016) - [i51]David P. Woodruff:
New Algorithms for Heavy Hitters in Data Streams. CoRR abs/1603.01733 (2016) - [i50]Yi Li, David P. Woodruff:
On Approximating Functions of the Singular Values in a Stream. CoRR abs/1604.08679 (2016) - [i49]Michael Kapralov, Vamsi K. Potluru, David P. Woodruff:
How to Fake Multiply by a Gaussian Matrix. CoRR abs/1606.05732 (2016) - [i48]Zhao Song, David P. Woodruff, Peilin Zhong:
Low Rank Approximation with Entrywise ℓ1-Norm Error. CoRR abs/1611.00898 (2016) - [i47]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Faster Kernel Ridge Regression Using Sketching and Preconditioning. CoRR abs/1611.03220 (2016) - [i46]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Sharper Bounds for Regression and Low-Rank Approximation with Regularization. CoRR abs/1611.03225 (2016) - [i45]Ravindran Kannan, Michael W. Mahoney, David P. Woodruff:
Recent Advances in Randomized Numerical Linear Algebra (NII Shonan Meeting 2016-10). NII Shonan Meet. Rep. 2016 (2016) - 2015
- [j13]Srikanta Tirthapura, David P. Woodruff:
A General Method for Estimating Correlated Aggregates Over a Data Stream. Algorithmica 73(2): 235-260 (2015) - [c81]Xiaoming Sun, David P. Woodruff:
Tight Bounds for Graph Problems in Insertion Streams. APPROX-RANDOM 2015: 435-448 - [c80]Kenneth L. Clarkson, David P. Woodruff:
Input Sparsity and Hardness for Robust Subspace Approximation. FOCS 2015: 310-329 - [c79]Marco Molinaro, David P. Woodruff, Grigory Yaroslavtsev:
Amplification of One-Way Information Complexity via Codes and Noise Sensitivity. ICALP (1) 2015: 960-972 - [c78]Omri Weinstein, David P. Woodruff:
The Simultaneous Communication of Disjointness with Applications to Data Streams. ICALP (1) 2015: 1082-1093 - [c77]Dirk Van Gucht, Ryan Williams, David P. Woodruff, Qin Zhang:
The Communication Complexity of Distributed Set-Joins with Applications to Matrix Multiplication. PODS 2015: 199-212 - [c76]Kenneth L. Clarkson, David P. Woodruff:
Sketching for M-Estimators: A Unified Approach to Robust Regression. SODA 2015: 921-939 - [i44]Mina Ghashami, Edo Liberty, Jeff M. Phillips, David P. Woodruff:
Frequent Directions : Simple and Deterministic Matrix Sketching. CoRR abs/1501.01711 (2015) - [i43]Maria-Florina Balcan, Yingyu Liang, Le Song, David P. Woodruff, Bo Xie:
Distributed Kernel Principal Component Analysis. CoRR abs/1503.06858 (2015) - [i42]Arturs Backurs, Piotr Indyk, Eric Price, Ilya P. Razenshteyn, David P. Woodruff:
Nearly-optimal bounds for sparse recovery in generic norms, with applications to $k$-median sketching. CoRR abs/1504.01076 (2015) - [i41]Christos Boutsidis, David P. Woodruff:
Communication-optimal Distributed Principal Component Analysis in the Column-partition Model. CoRR abs/1504.06729 (2015) - [i40]Hossein Esfandiari, MohammadTaghi Hajiaghayi, David P. Woodruff:
Applications of Uniform Sampling: Densest Subgraph and Beyond. CoRR abs/1506.04505 (2015) - [i39]Mark Braverman, Ankit Garg, Tengyu Ma, Huy L. Nguyen, David P. Woodruff:
Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality. CoRR abs/1506.07216 (2015) - [i38]Michael B. Cohen, Jelani Nelson, David P. Woodruff:
Optimal approximate matrix product in terms of stable rank. CoRR abs/1507.02268 (2015) - [i37]Kenneth L. Clarkson, David P. Woodruff:
Input Sparsity and Hardness for Robust Subspace Approximation. CoRR abs/1510.06073 (2015) - [i36]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff:
Beating CountSketch for Heavy Hitters in Insertion Streams. CoRR abs/1511.00661 (2015) - [i35]Alexandr Andoni, Jiecao Chen, Robert Krauthgamer, Bo Qin, David P. Woodruff, Qin Zhang:
On Sketching Quadratic Forms. CoRR abs/1511.06099 (2015) - [i34]Marco Molinaro, David P. Woodruff, Grigory Yaroslavtsev:
Amplification of One-Way Information Complexity via Codes and Noise Sensitivity. Electron. Colloquium Comput. Complex. TR15 (2015) - [i33]Omri Weinstein, David P. Woodruff:
The Simultaneous Communication of Disjointness with Applications to Data Streams. Electron. Colloquium Comput. Complex. TR15 (2015) - 2014
- [j12]Piotr Berman, Arnab Bhattacharyya, Elena Grigorescu, Sofya Raskhodnikova, David P. Woodruff, Grigory Yaroslavtsev:
Steiner transitive-closure spanners of low-dimensional posets. Comb. 34(3): 255-277 (2014) - [j11]David P. Woodruff:
Data Streams and Applications in Computer Science. Bull. EATCS 114 (2014) - [j10]David P. Woodruff:
Sketching as a Tool for Numerical Linear Algebra. Found. Trends Theor. Comput. Sci. 10(1-2): 1-157 (2014) - [c75]Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, David P. Woodruff, Grigory Yaroslavtsev:
Certifying Equality With Limited Interaction. APPROX-RANDOM 2014: 545-581 - [c74]Ravi Kannan, Santosh S. Vempala, David P. Woodruff:
Principal Component Analysis and Higher Correlations for Distributed Data. COLT 2014: 1040-1057 - [c73]Yi Li, Zhengyu Wang, David P. Woodruff:
Improved testing of low rank matrices. KDD 2014: 691-700 - [c72]David P. Woodruff:
Low Rank Approximation Lower Bounds in Row-Update Streams. NIPS 2014: 1781-1789 - [c71]Haim Avron, Huy L. Nguyen, David P. Woodruff:
Subspace Embeddings for the Polynomial Kernel. NIPS 2014: 2258-2266 - [c70]Yingyu Liang, Maria-Florina Balcan, Vandana Kanchanapally, David P. Woodruff:
Improved Distributed Principal Component Analysis. NIPS 2014: 3113-3121 - [c69]Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, David P. Woodruff, Grigory Yaroslavtsev:
Beyond set disjointness: the communication complexity of finding the intersection. PODC 2014: 106-113 - [c68]Michael Kapralov, David P. Woodruff:
Spanners and sparsifiers in dynamic streams. PODC 2014: 272-281 - [c67]Rasmus Pagh, Morten Stöckel, David P. Woodruff:
Is min-wise hashing optimal for summarizing set intersection? PODS 2014: 109-120 - [c66]David P. Woodruff, Qin Zhang:
An Optimal Lower Bound for Distinct Elements in the Message Passing Model. SODA 2014: 718-733 - [c65]Yi Li, Huy L. Nguyen, David P. Woodruff:
On Sketching Matrix Norms and the Top Singular Vector. SODA 2014: 1562-1581 - [c64]Yi Li, Huy L. Nguyen, David P. Woodruff:
Turnstile streaming algorithms might as well be linear sketches. STOC 2014: 174-183 - [c63]Christos Boutsidis, David P. Woodruff:
Optimal CUR matrix decompositions. STOC 2014: 353-362 - [c62]Yi Li, Xiaoming Sun, Chengu Wang, David P. Woodruff:
On the Communication Complexity of Linear Algebraic Problems in the Message Passing Model. DISC 2014: 499-513 - [i32]Alexandr Andoni, Robert Krauthgamer, David P. Woodruff:
The Sketching Complexity of Graph Cuts. CoRR abs/1403.7058 (2014) - [i31]Christos Boutsidis, David P. Woodruff:
Optimal CUR Matrix Decompositions. CoRR abs/1405.7910 (2014) - [i30]Yi Li, Xiaoming Sun, Chengu Wang, David P. Woodruff:
On The Communication Complexity of Linear Algebraic Problems in the Message Passing Model. CoRR abs/1407.4755 (2014) - [i29]Maria-Florina Balcan, Vandana Kanchanapally, Yingyu Liang, David P. Woodruff:
Improved Distributed Principal Component Analysis. CoRR abs/1408.5823 (2014) - [i28]David P. Woodruff:
Sketching as a Tool for Numerical Linear Algebra. CoRR abs/1411.4357 (2014) - [i27]Jiecao Chen, Bo Qin, David P. Woodruff, Qin Zhang:
A Sketching Algorithm for Spectral Graph Sparsification. CoRR abs/1412.8225 (2014) - 2013
- [j9]Benny Kimelfeld, Jan Vondrák, David P. Woodruff:
Multi-Tuple Deletion Propagation: Approximations and Complexity. Proc. VLDB Endow. 6(13): 1558-1569 (2013) - [j8]T. S. Jayram, David P. Woodruff:
Optimal Bounds for Johnson-Lindenstrauss Transforms and Streaming Problems with Subconstant Error. ACM Trans. Algorithms 9(3): 26:1-26:17 (2013) - [c61]Yi Li, David P. Woodruff:
A Tight Lower Bound for High Frequency Moment Estimation with Small Error. APPROX-RANDOM 2013: 623-638 - [c60]David P. Woodruff, Qin Zhang:
Subspace Embeddings and \(\ell_p\)-Regression Using Exponential Random Variables. COLT 2013: 546-567 - [c59]Haim Avron, Vikas Sindhwani, David P. Woodruff:
Sketching Structured Matrices for Faster Nonlinear Regression. NIPS 2013: 2994-3002 - [c58]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform and Faster Robust Linear Regression. SODA 2013: 466-477 - [c57]Eric Price, David P. Woodruff:
Lower Bounds for Adaptive Sparse Recovery. SODA 2013: 652-663 - [c56]Marco Molinaro, David P. Woodruff, Grigory Yaroslavtsev:
Beating the Direct Sum Theorem in Communication Complexity with Implications for Sketching. SODA 2013: 1738-1756 - [c55]Kenneth L. Clarkson, David P. Woodruff:
Low rank approximation and regression in input sparsity time. STOC 2013: 81-90 - [c54]Moritz Hardt, David P. Woodruff:
How robust are linear sketches to adaptive inputs? STOC 2013: 121-130 - [c53]David P. Woodruff, Qin Zhang:
When Distributed Computation Is Communication Expensive. DISC 2013: 16-30 - [i26]David P. Woodruff, Qin Zhang:
When Distributed Computation does not Help. CoRR abs/1304.4636 (2013) - [i25]David P. Woodruff, Qin Zhang:
Subspace Embeddings and ℓp-Regression Using Exponential Random Variables. CoRR abs/1305.5580 (2013) - 2012
- [j7]Kenneth L. Clarkson, Elad Hazan, David P. Woodruff:
Sublinear optimization for machine learning. J. ACM 59(5): 23:1-23:49 (2012) - [j6]David P. Woodruff:
A Quadratic Lower Bound for Three-Query Linear Locally Decodable Codes over Any Field. J. Comput. Sci. Technol. 27(4): 678-686 (2012) - [j5]Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. J. Mach. Learn. Res. 13: 3475-3506 (2012) - [j4]Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Transitive-Closure Spanners. SIAM J. Comput. 41(6): 1380-1425 (2012) - [j3]Arnab Bhattacharyya, Elena Grigorescu, Madhav Jha, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Lower Bounds for Local Monotonicity Reconstruction from Transitive-Closure Spanners. SIAM J. Discret. Math. 26(2): 618-646 (2012) - [c52]Jelani Nelson, Huy L. Nguyên, David P. Woodruff:
On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation. APPROX-RANDOM 2012: 627-638 - [c51]Srikanta Tirthapura, David P. Woodruff:
A General Method for Estimating Correlated Aggregates over a Data Stream. ICDE 2012: 162-173 - [c50]Michael W. Mahoney, Petros Drineas, Malik Magdon-Ismail, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. ICML 2012 - [c49]Eric Price, David P. Woodruff:
Applications of the Shannon-Hartley theorem to data streams and sparse recovery. ISIT 2012: 2446-2450 - [c48]Andrew McGregor, A. Pavan, Srikanta Tirthapura, David P. Woodruff:
Space-efficient estimation of statistics over sub-sampled streams. PODS 2012: 273-282 - [c47]Srikanta Tirthapura, David P. Woodruff:
Rectangle-efficient aggregation in spatial data streams. PODS 2012: 283-294 - [c46]Anna C. Gilbert, Brett Hemenway, Martin J. Strauss, David P. Woodruff, Mary Wootters:
Reusable low-error compressive sampling schemes through privacy. SSP 2012: 536-539 - [c45]David P. Woodruff, Qin Zhang:
Tight bounds for distributed functional monitoring. STOC 2012: 941-960 - [i24]Eric Price, David P. Woodruff:
Lower Bounds for Adaptive Sparse Recovery. CoRR abs/1205.3518 (2012) - [i23]Jelani Nelson, Huy L. Nguyên, David P. Woodruff:
On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation. CoRR abs/1206.5725 (2012) - [i22]Kenneth L. Clarkson, Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, Xiangrui Meng, David P. Woodruff:
The Fast Cauchy Transform: with Applications to Basis Construction, Regression, and Subspace Approximation in L1. CoRR abs/1207.4684 (2012) - [i21]Kenneth L. Clarkson, David P. Woodruff:
Low Rank Approximation and Regression in Input Sparsity Time. CoRR abs/1207.6365 (2012) - [i20]Moritz Hardt, David P. Woodruff:
How Robust are Linear Sketches to Adaptive Inputs? CoRR abs/1211.1056 (2012) - 2011
- [c44]Joshua Brody, David P. Woodruff:
Streaming Algorithms with One-Sided Estimation. APPROX-RANDOM 2011: 436-447 - [c43]Rolf Klein, Rainer Penninger, Christian Sohler, David P. Woodruff:
Tolerant Algorithms. ESA 2011: 736-747 - [c42]Piotr Indyk, Eric Price, David P. Woodruff:
On the Power of Adaptivity in Sparse Recovery. FOCS 2011: 285-294 - [c41]Eric Price, David P. Woodruff:
(1 + eps)-Approximate Sparse Recovery. FOCS 2011: 295-304 - [c40]Piotr Berman, Arnab Bhattacharyya, Elena Grigorescu, Sofya Raskhodnikova, David P. Woodruff, Grigory Yaroslavtsev:
Steiner Transitive-Closure Spanners of Low-Dimensional Posets. ICALP (1) 2011: 760-772 - [c39]Arnab Bhattacharyya, Piotr Indyk, David P. Woodruff, Ning Xie:
The Complexity of Linear Dependence Problems in Vector Spaces. ICS 2011: 496-508 - [c38]T. S. Jayram, David P. Woodruff:
Optimal Bounds for Johnson-Lindenstrauss Transforms and Streaming Problems with Sub-Constant Error. SODA 2011: 1-10 - [c37]David P. Woodruff:
Near-optimal private approximation protocols via a black box transformation. STOC 2011: 735-744 - [c36]Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff:
Fast moment estimation in data streams in optimal space. STOC 2011: 745-754 - [c35]Christian Sohler, David P. Woodruff:
Subspace embeddings for the L1-norm with applications. STOC 2011: 755-764 - [c34]Srikanta Tirthapura, David P. Woodruff:
Optimal Random Sampling from Distributed Streams Revisited. DISC 2011: 283-297 - [i19]Khanh Do Ba, Piotr Indyk, Eric Price, David P. Woodruff:
Lower Bounds for Sparse Recovery. CoRR abs/1106.0365 (2011) - [i18]Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, David P. Woodruff:
Fast approximation of matrix coherence and statistical leverage. CoRR abs/1109.3843 (2011) - [i17]Piotr Indyk, Eric Price, David P. Woodruff:
On the Power of Adaptivity in Sparse Recovery. CoRR abs/1110.3850 (2011) - [i16]Eric Price, David P. Woodruff:
(1+eps)-approximate Sparse Recovery. CoRR abs/1110.4414 (2011) - [i15]David P. Woodruff, Qin Zhang:
Tight Bounds for Distributed Functional Monitoring. CoRR abs/1112.5153 (2011) - 2010
- [j2]Alexandre V. Evfimievski, Ronald Fagin, David P. Woodruff:
Epistemic privacy. J. ACM 58(1): 2:1-2:45 (2010) - [c33]Arnab Bhattacharyya, Elena Grigorescu, Madhav Jha, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Lower Bounds for Local Monotonicity Reconstruction from Transitive-Closure Spanners. APPROX-RANDOM 2010: 448-461 - [c32]David P. Woodruff:
A Quadratic Lower Bound for Three-Query Linear Locally Decodable Codes over Any Field. APPROX-RANDOM 2010: 766-779 - [c31]Kenneth L. Clarkson, Elad Hazan, David P. Woodruff:
Sublinear Optimization for Machine Learning. FOCS 2010: 449-457 - [c30]David P. Woodruff:
Additive Spanners in Nearly Quadratic Time. ICALP (1) 2010: 463-474 - [c29]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
An optimal algorithm for the distinct elements problem. PODS 2010: 41-52 - [c28]Jelani Nelson, David P. Woodruff:
Fast Manhattan sketches in data streams. PODS 2010: 99-110 - [c27]Dan Feldman, Morteza Monemizadeh, Christian Sohler, David P. Woodruff:
Coresets and Sketches for High Dimensional Subspace Approximation Problems. SODA 2010: 630-649 - [c26]Morteza Monemizadeh, David P. Woodruff:
1-Pass Relative-Error Lp-Sampling with Applications. SODA 2010: 1143-1160 - [c25]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
On the Exact Space Complexity of Sketching and Streaming Small Norms. SODA 2010: 1161-1178 - [c24]Khanh Do Ba, Piotr Indyk, Eric Price, David P. Woodruff:
Lower Bounds for Sparse Recovery. SODA 2010: 1190-1197 - [i14]Daniel M. Kane, Jelani Nelson, Ely Porat, David P. Woodruff:
Fast Moment Estimation in Data Streams in Optimal Space. CoRR abs/1007.4191 (2010) - [i13]Kenneth L. Clarkson, Elad Hazan, David P. Woodruff:
Sublinear Optimization for Machine Learning. CoRR abs/1010.4408 (2010) - [i12]Piotr Berman, Arnab Bhattacharyya, Elena Grigorescu, Sofya Raskhodnikova, David P. Woodruff, Grigory Yaroslavtsev:
Steiner Transitive-Closure Spanners of d-Dimensional Posets. CoRR abs/1011.6100 (2010)
2000 – 2009
- 2009
- [c23]Alexandr Andoni, Khanh Do Ba, Piotr Indyk, David P. Woodruff:
Efficient Sketches for Earth-Mover Distance, with Applications. FOCS 2009: 324-330 - [c22]T. S. Jayram, David P. Woodruff:
The Data Stream Space Complexity of Cascaded Norms. FOCS 2009: 765-774 - [c21]David P. Woodruff:
The average-case complexity of counting distinct elements. ICDT 2009: 284-295 - [c20]Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Transitive-closure spanners. SODA 2009: 932-941 - [c19]Kenneth L. Clarkson, David P. Woodruff:
Numerical linear algebra in the streaming model. STOC 2009: 205-214 - [r1]David P. Woodruff:
Frequency Moments. Encyclopedia of Database Systems 2009: 1169-1170 - [i11]Jelani Nelson, David P. Woodruff:
A Near-Optimal Algorithm for L1-Difference. CoRR abs/0904.2027 (2009) - [i10]Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Transitive-Closure Spanners of the Hypercube and the Hypergrid. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [c18]David P. Woodruff:
Corruption and Recovery-Efficient Locally Decodable Codes. APPROX-RANDOM 2008: 584-595 - [c17]Alexandre V. Evfimievski, Ronald Fagin, David P. Woodruff:
Epistemic privacy. PODS 2008: 171-180 - [i9]Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, Sofya Raskhodnikova, David P. Woodruff:
Transitive-Closure Spanners. CoRR abs/0808.1787 (2008) - [i8]Daniel M. Kane, Jelani Nelson, David P. Woodruff:
Revisiting Norm Estimation in Data Streams. CoRR abs/0811.3648 (2008) - 2007
- [b1]David P. Woodruff:
Efficient and private distance approximation in the communication and streaming models. Massachusetts Institute of Technology, Cambridge, MA, USA, 2007 - [j1]David P. Woodruff, Sergey Yekhanin:
A Geometric Approach to Information-Theoretic Private Information Retrieval. SIAM J. Comput. 37(4): 1046-1056 (2007) - [c16]David P. Woodruff:
Revisiting the Efficiency of Malicious Two-Party Computation. EUROCRYPT 2007: 79-96 - [c15]Xiaoming Sun, David P. Woodruff:
The communication and streaming complexity of computing the longest common and increasing subsequences. SODA 2007: 336-345 - [i7]David P. Woodruff:
New Lower Bounds for General Locally Decodable Codes. Electron. Colloquium Comput. Complex. TR07 (2007) - 2006
- [c14]David P. Woodruff:
Better Approximations for the Minimum Common Integer Partition Problem. APPROX-RANDOM 2006: 248-259 - [c13]Zulfikar Ramzan, David P. Woodruff:
Fast Algorithms for the Free Riders Problem in Broadcast Encryption. CRYPTO 2006: 308-325 - [c12]Craig Gentry, Zulfikar Ramzan, David P. Woodruff:
Explicit Exclusive Set Systems with Applications to Broadcast Encryption. FOCS 2006: 27-38 - [c11]David P. Woodruff:
Lower Bounds for Additive Spanners, Emulators, and More. FOCS 2006: 389-398 - [c10]Piotr Indyk, David P. Woodruff:
Polylogarithmic Private Approximations and Efficient Matching. TCC 2006: 245-264 - [i6]Zulfikar Ramzan, David P. Woodruff:
Fast Algorithms for the Free Riders Problem in Broadcast Encryption. IACR Cryptol. ePrint Arch. 2006: 293 (2006) - [i5]David P. Woodruff:
Revisiting the Efficiency of Malicious Two-Party Computation. IACR Cryptol. ePrint Arch. 2006: 397 (2006) - 2005
- [c9]David P. Woodruff, Sergey Yekhanin:
A Geometric Approach to Information-Theoretic Private Information Retrieval. CCC 2005: 275-284 - [c8]Marten van Dijk, Robert Granger, Dan Page, Karl Rubin, Alice Silverberg, Martijn Stam, David P. Woodruff:
Practical Cryptography in High Dimensional Tori. EUROCRYPT 2005: 234-250 - [c7]Piotr Indyk, David P. Woodruff:
Optimal approximations of the frequency moments of data streams. STOC 2005: 202-208 - [i4]David P. Woodruff, Sergey Yekhanin:
A Geometric Approach to Information-Theoretic Private Information Retrieval. Electron. Colloquium Comput. Complex. TR05 (2005) - [i3]Piotr Indyk, David P. Woodruff:
Polylogarithmic Private Approximations and Efficient Matching. Electron. Colloquium Comput. Complex. TR05 (2005) - 2004
- [c6]David P. Woodruff, Jessica Staddon:
Private inference control. CCS 2004: 188-197 - [c5]Marten van Dijk, David P. Woodruff:
Asymptotically Optimal Communication for Torus-Based Cryptography. CRYPTO 2004: 157-178 - [c4]Hanson Zhou, David P. Woodruff:
Clustering via Matrix Powering. PODS 2004: 136-142 - [c3]David P. Woodruff:
Optimal space lower bounds for all frequency moments. SODA 2004: 167-175 - [i2]David P. Woodruff, Jessica Staddon:
Private Inference Control. IACR Cryptol. ePrint Arch. 2004: 130 (2004) - [i1]Marten van Dijk, Robert Granger, Dan Page, Karl Rubin, Alice Silverberg, Martijn Stam, David P. Woodruff:
Practical Cryptography in High Dimensional Tori. IACR Cryptol. ePrint Arch. 2004: 352 (2004) - 2003
- [c2]Piotr Indyk, David P. Woodruff:
Tight Lower Bounds for the Distinct Elements Problem. FOCS 2003: 283-288 - 2002
- [c1]David P. Woodruff, Marten van Dijk:
Cryptography in an Unbounded Computational Model. EUROCRYPT 2002: 149-164
Coauthor Index
William Swartworth
aka: William J. Swartworth
aka: William J. Swartworth
[j41] [i209] [c254] [c253] [c250] [c247] [c240] [c239] [c237] [c236] [i204] [i203] [i199] [i194] [i189] [i186] [i181] [c231] [c226] [c225] [c218] [c216] [c215] [c214] [c209] [i177] [i172] [i171] [i167] [i162] [i160] [c203] [c201] [c200] [i156] [i155] [i151] [i141] [i140] [c178] [c164] [i131] [i129] [i119] [c133] [i87] [i86]
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