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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
- 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) - [c203]Yi Li, David P. Woodruff:
The Product of Gaussian Matrices Is Close to Gaussian. APPROX-RANDOM 2021: 35:1-35:22 - [c202]Akshay Kamath, Eric Price, David P. Woodruff:
A Simple Proof of a New Set Disjointness with Applications to Data Streams. Computational Complexity Conference 2021: 37:1-37:24 - [c201]Praneeth Kacham, David P. Woodruff:
Reduced-Rank Regression with Operator Norm Error. COLT 2021: 2679-2716 - [c200]Yi Li, David P. Woodruff, Taisuke Yasuda:
Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing. COLT 2021: 3111-3195 - [c199]Cyrus Rashtchian, David P. Woodruff, Peng Ye, Hanlin Zhu:
Average-Case Communication Complexity of Statistical Problems. COLT 2021: 3859-3886 - [c198]David P. Woodruff:
A Very Sketchy Talk (Invited Talk). ICALP 2021: 6:1-6:8 - [c197]David P. Woodruff, Samson Zhou:
Separations for Estimating Large Frequency Moments on Data Streams. ICALP 2021: 112:1-112:21 - [c196]Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou:
Learning a Latent Simplex in Input Sparsity Time. ICLR 2021 - [c195]Zhili Feng, Praneeth Kacham, David P. Woodruff:
Dimensionality Reduction for the Sum-of-Distances Metric. ICML 2021: 3220-3229 - [c194]Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye:
In-Database Regression in Input Sparsity Time. ICML 2021: 4797-4806 - [c193]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 - [c192]Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David P. Woodruff:
Single Pass Entrywise-Transformed Low Rank Approximation. ICML 2021: 4982-4991 - [c191]Alexander Munteanu, Simon Omlor, David P. Woodruff:
Oblivious Sketching for Logistic Regression. ICML 2021: 7861-7871 - [c190]Zhao Song, David P. Woodruff, Zheng Yu, Lichen Zhang:
Fast Sketching of Polynomial Kernels of Polynomial Degree. ICML 2021: 9812-9823 - [c189]Cameron Musco, Christopher Musco, David P. Woodruff:
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation. ITCS 2021: 6:1-6:20 - [c188]Graham Cormode, Charlie Dickens, David P. Woodruff:
Subspace Exploration: Bounds on Projected Frequency Estimation. PODS 2021: 273-284 - [c187]Arvind V. Mahankali, David P. Woodruff:
Optimal ℓ1 Column Subset Selection and a Fast PTAS for Low Rank Approximation. SODA 2021: 560-578 - [c186]Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff:
Hutch++: Optimal Stochastic Trace Estimation. SOSA 2021: 142-155 - [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 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. 27: 139 (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, 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]