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Semantic ScholarDavid P. Woodruff
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- affiliation: Carnegie Mellon University, PA, USA
- affiliation (former): IBM Almaden Research Center
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2020 – today
- 2020
[j26]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)- [c170]Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang:
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. ICLR 2020 - [c169]Tanqiu Jiang, Yi Li, Honghao Lin, Yisong Ruan, David P. Woodruff:
Learning-Augmented Data Stream Algorithms. ICLR 2020 - [c168]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
Graph Spanners in the Message-Passing Model. ITCS 2020: 77:1-77:18 - [c167]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-Deterministic Streaming. ITCS 2020: 79:1-79:25 - [c166]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 - [c165]Yi Li, Ruosong Wang, David P. Woodruff:
Tight Bounds for the Subspace Sketch Problem with Applications. SODA 2020: 1655-1674 - [c164]Santosh S. Vempala, Ruosong Wang, David P. Woodruff:
The Communication Complexity of Optimization. SODA 2020: 1733-1752 - [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 - [i120]Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang:
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. CoRR abs/2002.08202 (2020) - [i119]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) - [i118]Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev:
A Framework for Adversarially Robust Streaming Algorithms. CoRR abs/2003.14265 (2020) - [i117]Zhao Song, David P. Woodruff, Peilin Zhong:
Average Case Column Subset Selection for Entrywise 𝓁1-Norm Loss. CoRR abs/2004.07986 (2020) - [i116]Sepideh Mahabadi, Ilya P. Razenshteyn, David P. Woodruff, Samson Zhou:
Non-Adaptive Adaptive Sampling on Turnstile Streams. CoRR abs/2004.10969 (2020) - [i115]Yi Li, David P. Woodruff:
Input-Sparsity Low Rank Approximation in Schatten Norm. CoRR abs/2004.12646 (2020)
2010 – 2019
- 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. Symposium on Computational Geometry 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, 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 𝓁1-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 - [i114]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. CoRR abs/1902.10328 (2019) - [i113]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) - [i112]Srikanta Tirthapura, David P. Woodruff:
Optimal Random Sampling from Distributed Streams Revisited. CoRR abs/1903.12065 (2019) - [i111]Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, David P. Woodruff:
Weighted Reservoir Sampling from Distributed Streams. CoRR abs/1904.04126 (2019) - [i110]Yi Li, Ruosong Wang, David P. Woodruff:
Tight Bounds for the Subspace Sketch Problem with Applications. CoRR abs/1904.05543 (2019) - [i109]Cameron Musco, Christopher Musco, David P. Woodruff:
Low-Rank Approximation from Communication Complexity. CoRR abs/1904.09841 (2019) - [i108]Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff:
Dimensionality Reduction for Tukey Regression. CoRR abs/1905.05376 (2019) - [i107]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) - [i106]David P. Woodruff, Guang Yang:
Separating k-Player from t-Player One-Way Communication, with Applications to Data Streams. CoRR abs/1905.07135 (2019) - [i105]Piotr Indyk, Ali Vakilian, Tal Wagner, David P. Woodruff:
Sample-Optimal Low-Rank Approximation of Distance Matrices. CoRR abs/1906.00339 (2019) - [i104]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR abs/1906.04661 (2019) - [i103]Xiaoming Sun, David P. Woodruff, Guang Yang, Jialin Zhang:
Querying a Matrix through Matrix-Vector Products. CoRR abs/1906.05736 (2019) - [i102]Santosh S. Vempala, Ruosong Wang, David P. Woodruff:
The Communication Complexity of Optimization. CoRR abs/1906.05832 (2019) - [i101]Rajesh Jayaram, David P. Woodruff:
Towards Optimal Moment Estimation in Streaming and Distributed Models. CoRR abs/1907.05816 (2019) - [i100]Michael Kapralov, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh:
Oblivious Sketching of High-Degree Polynomial Kernels. CoRR abs/1909.01410 (2019) - [i99]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
The Query Complexity of Mastermind with 𝓁p Distances. CoRR abs/1909.10668 (2019) - [i98]Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang:
Total Least Squares Regression in Input Sparsity Time. CoRR abs/1909.12441 (2019) - [i97]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) - [i96]Manuel Fernandez, David P. Woodruff, Taisuke Yasuda:
Graph Spanners in the Message-Passing Model. CoRR abs/1911.05991 (2019) - [i95]Frank Ban, David P. Woodruff, Qiuyi Zhang:
Regularized Weighted Low Rank Approximation. CoRR abs/1911.06958 (2019) - [i94]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-deterministic Streaming. CoRR abs/1911.11368 (2019) - [i93]Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff:
Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation. CoRR abs/1912.04177 (2019) - [i92]Xiaofei Shi, David P. Woodruff:
Sublinear Time Numerical Linear Algebra for Structured Matrices. CoRR abs/1912.06060 (2019) - [i91]Zhili Feng, Praneeth Kacham, David P. Woodruff:
Strong Coresets for Subspace Approximation and k-Median in Nearly Linear Time. CoRR abs/1912.12003 (2019) - [i90]Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty, David P. Woodruff:
Pseudo-deterministic Streaming. Electronic Colloquium on Computational Complexity (ECCC) 26: 177 (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 - [i89]Ruosong Wang, David P. Woodruff:
Tight Bounds for 𝓁p Oblivious Subspace Embeddings. CoRR abs/1801.04414 (2018) - [i88]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. CoRR abs/1803.02270 (2018) - [i87]Rajesh Jayaram, David P. Woodruff:
Data Streams with Bounded Deletions. CoRR abs/1803.08777 (2018) - [i86]Vasileios Nakos, Xiaofei Shi, David P. Woodruff, Hongyang Zhang:
Improved Algorithms for Adaptive Compressed Sensing. CoRR abs/1804.09673 (2018) - [i85]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) - [i84]Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff:
On Coresets for Logistic Regression. CoRR abs/1805.08571 (2018) - [i83]Sumit Ganguly, David P. Woodruff:
High Probability Frequency Moment Sketches. CoRR abs/1805.10885 (2018) - [i82]Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff:
On Sketching the q to p norms. CoRR abs/1806.06429 (2018) - [i81]John Hainline, Brendan Juba, Hai S. Le, David P. Woodruff:
Conditional Sparse 𝓁p-norm Regression With Optimal Probability. CoRR abs/1806.10222 (2018) - [i80]David P. Woodruff, Qin Zhang:
Distributed Statistical Estimation of Matrix Products with Applications. CoRR abs/1807.00878 (2018) - [i79]Graham Cormode, Charlie Dickens, David P. Woodruff:
Leveraging Well-Conditioned Bases: Streaming \& Distributed Summaries in Minkowski p-Norms. CoRR abs/1807.02571 (2018) - [i78]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) - [i77]Rajesh Jayaram, David P. Woodruff:
Perfect Lp Sampling in a Data Stream. CoRR abs/1808.05497 (2018) - [i76]Christian Sohler, David P. Woodruff:
Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimension. CoRR abs/1809.02961 (2018) - [i75]Ainesh Bakshi, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Distance Matrices. CoRR abs/1809.06986 (2018) - [i74]Maria-Florina Balcan, Yi Li, David P. Woodruff, Hongyang Zhang:
Testing Matrix Rank, Optimally. CoRR abs/1810.08171 (2018) - [i73]Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Entrywise Low Rank Approximation. CoRR abs/1811.01442 (2018) - [i72]Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff:
Learning Two Layer Rectified Neural Networks in Polynomial Time. CoRR abs/1811.01885 (2018) - [i71]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. Electronic Colloquium on Computational Complexity (ECCC) 25: 103 (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 - [i70]Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang:
Communication-Optimal Distributed Clustering. CoRR abs/1702.00196 (2017) - [i69]Yi Li, David P. Woodruff:
Embeddings of Schatten Norms with Applications to Data Streams. CoRR abs/1702.05626 (2017) - [i68]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) - [i67]Cameron Musco, David P. Woodruff:
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices. CoRR abs/1704.03371 (2017) - [i66]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) - [i65]Zhao Song, David P. Woodruff, Peilin Zhong:
Relative Error Tensor Low Rank Approximation. CoRR abs/1704.08246 (2017) - [i64]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) - [i63]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) - [i62]Eric Price, Zhao Song, David P. Woodruff:
Fast Regression with an $\ell_\infty$ Guarantee.