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A Very Sketchy Talk (Invited Talk)

Author David P. Woodruff

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Author Details

David P. Woodruff
  • Carnegie Mellon University, Pittsburgh, PA, USA

Funding

  • Woodruff, David P.: Supported by the Office of Naval Research (ONR) grant N00014-18-1-2562, the National Science Foundation (NSF) under Grant No. CCF-1815840, and a Simons Investigator Award.

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David P. Woodruff. A Very Sketchy Talk (Invited Talk). In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 6:1-6:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ICALP.2021.6

Abstract

We give an overview of dimensionality reduction methods, or sketching, for a number of problems in optimization, first surveying work using these methods for classical problems, which gives near optimal algorithms for regression, low rank approximation, and natural variants. We then survey recent work applying sketching to column subset selection, kernel methods, sublinear algorithms for structured matrices, tensors, trace estimation, and so on. The focus is on fast algorithms. This is a short survey accompanying an invited talk at ICALP, 2021.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • dimensionality reduction
  • optimization
  • randomized numerical linear algebra
  • sketching

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References

  1. Thomas D. Ahle, Michael Kapralov, Jakob Bæk Tejs Knudsen, Rasmus Pagh, Ameya Velingker, David P. Woodruff, and Amir Zandieh. Oblivious sketching of high-degree polynomial kernels. In Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, SODA 2020, Salt Lake City, UT, USA, January 5-8, 2020, pages 141-160, 2020. Google Scholar
  2. Haim Avron, Kenneth L. Clarkson, and David P. Woodruff. Faster kernel ridge regression using sketching and preconditioning. SIAM J. Matrix Anal. Appl., 38(4):1116-1138, 2017. Google Scholar
  3. Haim Avron, Kenneth L. Clarkson, and David P. Woodruff. Sharper bounds for regularized data fitting. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2017, August 16-18, 2017, Berkeley, CA, USA, pages 27:1-27:22, 2017. Google Scholar
  4. Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, and Samson Zhou. Learning a latent simplex in input-sparsity time, 2021. Google Scholar
  5. Ainesh Bakshi, Nadiia Chepurko, and David P. Woodruff. Robust and sample optimal algorithms for PSD low rank approximation. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16-19, 2020, pages 506-516, 2020. Google Scholar
  6. Ainesh Bakshi and David P. Woodruff. Sublinear time low-rank approximation of distance matrices. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 3786-3796, 2018. Google Scholar
  7. Frank Ban, David P. Woodruff, and Richard Zhang. Regularized weighted low rank approximation. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 4061-4071, 2019. Google Scholar
  8. Jean Bourgain, Sjoerd Dirksen, and Jelani Nelson. Toward a unified theory of sparse dimensionality reduction in euclidean space. In Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC 2015, Portland, OR, USA, June 14-17, 2015, pages 499-508, 2015. Google Scholar
  9. Christos Boutsidis and David P. Woodruff. Optimal CUR matrix decompositions. SIAM J. Comput., 46(2):543-589, 2017. Google Scholar
  10. Christos Boutsidis, David P. Woodruff, and Peilin Zhong. Optimal principal component analysis in distributed and streaming models. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, pages 236-249, 2016. Google Scholar
  11. Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, and Samson Zhou. Near optimal linear algebra in the online and sliding window models, 2020. URL: http://arxiv.org/abs/1805.03765.
  12. Moses Charikar, Kevin C. Chen, and Martin Farach-Colton. Finding frequent items in data streams. Theor. Comput. Sci., 312(1):3-15, 2004. Google Scholar
  13. Kenneth L. Clarkson and David P. Woodruff. Numerical linear algebra in the streaming model. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, May 31 - June 2, 2009, pages 205-214, 2009. Google Scholar
  14. Kenneth L. Clarkson and David P. Woodruff. Low rank approximation and regression in input sparsity time. In Symposium on Theory of Computing Conference, STOC'13, Palo Alto, CA, USA, June 1-4, 2013, pages 81-90, 2013. Google Scholar
  15. Kenneth L. Clarkson and David P. Woodruff. Input sparsity and hardness for robust subspace approximation, 2015. URL: http://arxiv.org/abs/1510.06073.
  16. Kenneth L. Clarkson and David P. Woodruff. Sketching for M-estimators: A unified approach to robust regression. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2015, San Diego, CA, USA, January 4-6, 2015, pages 921-939, 2015. Google Scholar
  17. Michael B. Cohen. Nearly tight oblivious subspace embeddings by trace inequalities. In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016, Arlington, VA, USA, January 10-12, 2016, pages 278-287, 2016. Google Scholar
  18. Michael B. Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu. Dimensionality reduction for k-means clustering and low rank approximation. In Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC 2015, Portland, OR, USA, June 14-17, 2015, pages 163-172, 2015. Google Scholar
  19. Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford. Uniform sampling for matrix approximation. In Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, ITCS 2015, Rehovot, Israel, January 11-13, 2015, pages 181-190, 2015. Google Scholar
  20. Michael B. Cohen, Jelani Nelson, and David P. Woodruff. Optimal approximate matrix product in terms of stable rank, 2016. URL: http://arxiv.org/abs/1507.02268.
  21. Zhili Feng, Praneeth Kacham, and David P. Woodruff. Strong coresets for subspace approximation and k-median in nearly linear time. CoRR, abs/1912.12003, 2019. Google Scholar
  22. Piotr Indyk, Ali Vakilian, Tal Wagner, and David P. Woodruff. Sample-optimal low-rank approximation of distance matrices. In Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA, pages 1723-1751, 2019. Google Scholar
  23. Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, and Raman Arora. Communication-efficient distributed sgd with sketching. arXiv preprint, 2019. URL: http://arxiv.org/abs/1903.04488.
  24. Xiangrui Meng and Michael W. Mahoney. Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression. In Symposium on Theory of Computing Conference, STOC'13, Palo Alto, CA, USA, June 1-4, 2013, pages 91-100, 2013. Google Scholar
  25. Raphael A. Meyer, Cameron Musco, Christopher Musco, and David P. Woodruff. Hutch++: Optimal stochastic trace estimation. In 4th Symposium on Simplicity in Algorithms, SOSA 2021, Virtual Conference, January 11-12, 2021, pages 142-155, 2021. Google Scholar
  26. Cameron Musco and David P. Woodruff. Sublinear time low-rank approximation of positive semidefinite matrices. In 58th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2017, Berkeley, CA, USA, October 15-17, 2017, pages 672-683, 2017. Google Scholar
  27. Jelani Nelson and Huy L. Nguyen. OSNAP: faster numerical linear algebra algorithms via sparser subspace embeddings. In 54th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2013, 26-29 October, 2013, Berkeley, CA, USA, pages 117-126, 2013. Google Scholar
  28. Ninh Pham and Rasmus Pagh. Fast and scalable polynomial kernels via explicit feature maps. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, pages 239-247, 2013. Google Scholar
  29. Mert Pilanci and Martin J. Wainwright. Iterative hessian sketch: Fast and accurate solution approximation for constrained least-squares, 2014. URL: http://arxiv.org/abs/1411.0347.
  30. Eric Price, Zhao Song, and David P. Woodruff. Fast regression with an dollarell_inftydollar guarantee. In 44th International Colloquium on Automata, Languages, and Programming, ICALP 2017, July 10-14, 2017, Warsaw, Poland, pages 59:1-59:14, 2017. Google Scholar
  31. Ilya P. Razenshteyn, Zhao Song, and David P. Woodruff. Weighted low rank approximations with provable guarantees. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, pages 250-263, 2016. Google Scholar
  32. Tamás Sarlós. Improved approximation algorithms for large matrices via random projections. In 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), 21-24 October 2006, Berkeley, California, USA, Proceedings, pages 143-152, 2006. Google Scholar
  33. Xiaofei Shi and David P. Woodruff. Sublinear time numerical linear algebra for structured matrices. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 4918-4925, 2019. Google Scholar
  34. Christian Sohler and David P. Woodruff. Subspace embeddings for the l_1-norm with applications. In Proceedings of the 43rd ACM Symposium on Theory of Computing, STOC 2011, San Jose, CA, USA, 6-8 June 2011, pages 755-764, 2011. Google Scholar
  35. Christian Sohler and David P. Woodruff. Strong coresets for k-median and subspace approximation: Goodbye dimension. In 59th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, October 7-9, 2018, pages 802-813, 2018. Google Scholar
  36. Zhao Song, David P. Woodruff, and Peilin Zhong. Towards a zero-one law for entrywise low rank approximation. CoRR, abs/1811.01442, 2018. URL: http://arxiv.org/abs/1811.01442.
  37. Zhao Song, David P. Woodruff, and Peilin Zhong. Relative error tensor low rank approximation. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, San Diego, California, USA, January 6-9, 2019, pages 2772-2789, 2019. Google Scholar
  38. Ruosong Wang and David P. Woodruff. Tight bounds for lp oblivious subspace embeddings. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019, San Diego, California, USA, January 6-9, 2019, pages 1825-1843, 2019. Google Scholar
  39. David P. Woodruff. Sketching as a tool for numerical linear algebra. Found. Trends Theor. Comput. Sci., 10(1-2):1-157, 2014. Google Scholar
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