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ORCIDDavid P. Woodruff
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- affiliation: Carnegie Mellon University, PA, USA
- affiliation (former): IBM Almaden Research Center
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2020 – today
- 2023
[c232]Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, Shufan Zhang:
Recovery from Non-Decomposable Distance Oracles. ITCS 2023: 73:1-73:22- 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) - [c231]Yi Li, Honghao Lin, David P. Woodruff, Yuheng Zhang:
Streaming Algorithms with Large Approximation Factors. APPROX/RANDOM 2022: 13:1-13:23 - [c230]Sepideh Mahabadi, David P. Woodruff, Samson Zhou:
Adaptive Sketches for Robust Regression with Importance Sampling. APPROX/RANDOM 2022: 31:1-31:21 - [c229]Deanna Needell, William Swartworth, David P. Woodruff:
Testing Positive Semidefiniteness Using Linear Measurements. FOCS 2022: 87-97 - [c228]David P. Woodruff, Taisuke Yasuda:
High-Dimensional Geometric Streaming in Polynomial Space. FOCS 2022: 732-743 - [c227]Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda:
Active Linear Regression for ℓp Norms and Beyond. FOCS 2022: 744-753 - [c226]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 - [c225]Jon C. Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou:
Learning-Augmented $k$-means Clustering. ICLR 2022 - [c224]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff, Samson Zhou:
Fast Regression for Structured Inputs. ICLR 2022 - [c223]Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. ICML 2022: 3879-3900 - [c222]Praneeth Kacham, David P. Woodruff:
Sketching Algorithms and Lower Bounds for Ridge Regression. ICML 2022: 10539-10556 - [c221]Honghao Lin, Tian Luo, David P. Woodruff:
Learning Augmented Binary Search Trees. ICML 2022: 13431-13440 - [c220]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 - [c219]David P. Woodruff, Amir Zandieh:
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time. ICML 2022: 23933-23964 - [c218]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 - [c217]Michael Kapralov, Amulya Musipatla, Jakab Tardos, David P. Woodruff, Samson Zhou:
Noisy Boolean Hidden Matching with Applications. ITCS 2022: 91:1-91:19 - [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 Algor