Yulin Wan

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All published works

Action	Title	Year	Authors
+	Learning the Positions in CountSketch	2023	Simin Liu Tianrui Liu Ali Vakilian Yulin Wan David P. Woodruff
+	Extending and Improving Learned CountSketch	2020	Simin Liu Tianrui Liu Ali Vakilian Yulin Wan David P. Woodruff
+	Learning the Positions in CountSketch	2020	Simin Liu Tianrui Liu Ali Vakilian Yulin Wan David P. Woodruff

Common Coauthors

Coauthor	Papers Together
Ali Vakilian	3
David P. Woodruff	3
Tianrui Liu	3
Simin Liu	2
Simin Liu	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Hyperspectral Image Dataset for Benchmarking on Salient Object Detection	2018	Nevrez İmamoğlu Yu Oishi Xiaoqiang Zhang Guanqun Ding Yuming Fang Toru Kouyama Ryosuke Nakamura	2
+	Learning-Based Low-Rank Approximations	2019	Piotr Indyk Ali Vakilian Yuan Yang	2
+ PDF Chat	NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings	2015	Chinmay Hegde Aswin C. Sankaranarayanan Wotao Yin Richard G. Baraniuk	2
+	Composable Sketches for Functions of Frequencies: Beyond the Worst Case	2020	Edith Cohen Ofir Geri Rasmus Pagh	2
+ PDF Chat	Low-Rank Approximation and Regression in Input Sparsity Time	2017	Kenneth L. Clarkson David P. Woodruff	2
+ PDF Chat	Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression	2013	Xiangrui Meng Michael W. Mahoney	2
+ PDF Chat	Performance of Johnson-Lindenstrauss transform for <i>k</i> -means and <i>k</i> -medians clustering	2019	Konstantin Makarychev Yury Makarychev Ilya Razenshteyn	2
+ PDF Chat	Lower Bounds for Oblivious Subspace Embeddings	2014	Jelani Nelson Huy L. Nguyễn	1
+	Introduction to the non-asymptotic analysis of random matrices	2010	Roman Vershynin	1
+	Limiting Form of the Sample Covariance Eigenspectrum in PCA and Kernel PCA	2003	David C. Hoyle Magnus Rattray	1
+	A Randomized Algorithm for the Approximation of Matrices	2006	Per‐Gunnar Martinsson Vladimir Rockhlin Mark Tygert	1
+	Sketching as a Tool for Numerical Linear Algebra	2014	David P. Woodruff	1
+	Sharper Bounds for Regularized Data Fitting	2016	Haim Avron Kenneth L. Clarkson David P. Woodruff	1
+	Compressed Sensing using Generative Models	2017	Ashish Bora Ajil Jalal Eric Price Alexandros G. Dimakis	1
+	A Survey on Learning to Hash	2016	Jingdong Wang Ting Zhang Jingkuan Song Nicu Sebe Heng Tao Shen	1
+	Randomized algorithms for matrices and data	2011	Michael W. Mahoney	1
+	(Learned) Frequency Estimation Algorithms under Zipfian Distribution	2019	Anders Aamand Piotr Indyk Ali Vakilian	1
+	PyTorch: An Imperative Style, High-Performance Deep Learning Library	2019	Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga	1
+ PDF Chat	A deep learning approach to structured signal recovery	2015	Ali Mousavi Ankit Patel Richard G. Baraniuk	1
+	Learning-based Support Estimation in Sublinear Time	2021	Talya Eden Piotr Indyk Shyam Narayanan Ronitt Rubinfeld Sandeep Silwal Tal Wagner	1
+ PDF Chat	On the distribution of the largest eigenvalue in principal components analysis	2001	Iain M. Johnstone	1
+	ASYMPTOTICS OF SAMPLE EIGENSTRUCTURE FOR A LARGE DIMENSIONAL SPIKED COVARIANCE MODEL	2007	Debashis Paul	1
+	Financial applications of random matrix theory: a short review	2015	Jean-Phillipe Bouchaud Marc Potters	1
+	Extreme eigenvalues of sparse, heavy tailed random matrices	2016	Antonio Auffinger Si Tang	1
+	Principal-component-analysis eigenvalue spectra from data with symmetry-breaking structure	2004	David C. Hoyle Magnus Rattray	1
+	Robust regression using iteratively reweighted least-squares	1977	Paul W. Holland Roy E. Welsch	1
+	Eigenvalues of large sample covariance matrices of spiked population models	2005	Jinho Baik Jack W. Silverstein	1
+ PDF Chat	OSNAP: Faster Numerical Linear Algebra Algorithms via Sparser Subspace Embeddings	2013	Jelani Nelson Huy L. Nguyễn	1