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Michal Derezinski
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2020 – today
- 2024
- [j4]Michal Derezinski, Elizaveta Rebrova:
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition. SIAM J. Math. Data Sci. 6(1): 127-153 (2024) - [c29]Michal Derezinski
, Michael W. Mahoney
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. KDD 2024: 6470-6479 - [c28]Shabarish Chenakkod
Optimal Embedding Dimension for Sparse Subspace Embeddings. STOC 2024: 1106-1117 - [c27]Michal Derezinski
Solving Dense Linear Systems Faster Than via Preconditioning. STOC 2024: 1118-1129 - [i40]Yongyi Yang, Jiaming Yang, Wei Hu, Michal Derezinski
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks. CoRR abs/2403.18142 (2024) - [i39]Sachin Garg, Albert S. Berahas, Michal Derezinski
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients. CoRR abs/2404.14758 (2024) - [i38]Sachin Garg, Kevin Tan, Michal Derezinski
Distributed Least Squares in Small Space via Sketching and Bias Reduction. CoRR abs/2405.05343 (2024) - [i37]Michal Derezinski
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems. CoRR abs/2405.05818 (2024) - [i36]Michal Derezinski
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning. CoRR abs/2405.05865 (2024) - [i35]Ruichen Jiang, Michal Derezinski
Stochastic Newton Proximal Extragradient Method. CoRR abs/2406.01478 (2024) - [i34]Michal Derezinski
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. CoRR abs/2406.11151 (2024) - 2023
- [j3]Sen Na
Hessian averaging in stochastic Newton methods achieves superlinear convergence. Math. Program. 201(1): 473-520 (2023) - [c26]Michal Derezinski:
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. COLT 2023: 3137-3172 - [i33]Riley Murray
Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. CoRR abs/2302.11474 (2023) - [i32]Younghyun Cho, James Weldon Demmel, Michal Derezinski
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems. CoRR abs/2308.15720 (2023) - [i31]Shabarish Chenakkod, Michal Derezinski
Optimal Embedding Dimension for Sparse Subspace Embeddings. CoRR abs/2311.10680 (2023) - [i30]Michal Derezinski
Solving Dense Linear Systems Faster than via Preconditioning. CoRR abs/2312.08893 (2023) - 2022
- [j2]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Unbiased estimators for random design regression. J. Mach. Learn. Res. 23: 167:1-167:46 (2022) - [c25]Nima Anari
Domain Sparsification of Discrete Distributions Using Entropic Independence. ITCS 2022: 5:1-5:23 - [i29]Sen Na, Michal Derezinski
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence. CoRR abs/2204.09266 (2022) - [i28]Michal Derezinski
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches. CoRR abs/2206.02702 (2022) - [i27]Michal Derezinski:
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. CoRR abs/2206.10291 (2022) - [i26]Michal Derezinski
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition. CoRR abs/2208.09585 (2022) - 2021
- [c24]Xue Chen, Michal Derezinski:
Query complexity of least absolute deviation regression via robust uniform convergence. COLT 2021: 1144-1179 - [c23]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. COLT 2021: 1467-1510 - [c22]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). IJCAI 2021: 4765-4769 - [c21]Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney:
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. NeurIPS 2021: 2835-2847 - [c20]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing communication rounds for distributed learning. UAI 2021: 632-642 - [i25]Xue Chen, Michal Derezinski:
Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence. CoRR abs/2102.02322 (2021) - [i24]Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney:
LocalNewton: Reducing Communication Bottleneck for Distributed Learning. CoRR abs/2105.07320 (2021) - [i23]Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney:
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. CoRR abs/2107.07480 (2021) - [i22]Nima Anari, Michal Derezinski, Thuy-Duong Vuong, Elizabeth Yang:
Domain Sparsification of Discrete Distributions using Entropic Independence. CoRR abs/2109.06442 (2021) - 2020
- [c19]Mojmir Mutny, Michal Derezinski, Andreas Krause:
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling. AISTATS 2020: 3110-3120 - [c18]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Bayesian experimental design using regularized determinantal point processes. AISTATS 2020: 3197-3207 - [c17]Nima Anari
Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases. FOCS 2020: 1331-1344 - [c16]Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. NeurIPS 2020 - [c15]Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. NeurIPS 2020 - [c14]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. NeurIPS 2020 - [c13]Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney:
Precise expressions for random projections: Low-rank approximation and randomized Newton. NeurIPS 2020 - [c12]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. NeurIPS 2020 - [i21]Michal Derezinski, Rajiv Khanna, Michael W. Mahoney:
Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. CoRR abs/2002.09073 (2020) - [i20]Nima Anari, Michal Derezinski:
Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases. CoRR abs/2004.09079 (2020) - [i19]Michal Derezinski, Michael W. Mahoney:
Determinantal Point Processes in Randomized Numerical Linear Algebra. CoRR abs/2005.03185 (2020) - [i18]Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney:
Precise expressions for random projections: Low-rank approximation and randomized Newton. CoRR abs/2006.10653 (2020) - [i17]Daniele Calandriello, Michal Derezinski, Michal Valko:
Sampling from a k-DPP without looking at all items. CoRR abs/2006.16947 (2020) - [i16]Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney:
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. CoRR abs/2007.01327 (2020) - [i15]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. CoRR abs/2011.10695 (2020)
2010 – 2019
- 2019
- [c11]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Correcting the bias in least squares regression with volume-rescaled sampling. AISTATS 2019: 944-953 - [c10]Michal Derezinski:
Fast determinantal point processes via distortion-free intermediate sampling. COLT 2019: 1029-1049 - [c9]Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth:
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. COLT 2019: 1050-1069 - [c8]Michal Derezinski, Michael W. Mahoney:
Distributed estimation of the inverse Hessian by determinantal averaging. NeurIPS 2019: 11401-11411 - [c7]Michal Derezinski, Daniele Calandriello, Michal Valko:
Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS 2019: 11542-11554 - [i14]Michal Derezinski, Kenneth L. Clarkson, Michael W. Mahoney, Manfred K. Warmuth:
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. CoRR abs/1902.00995 (2019) - [i13]Michal Derezinski, Michael W. Mahoney:
Distributed estimation of the inverse Hessian by determinantal averaging. CoRR abs/1905.11546 (2019) - [i12]Michal Derezinski, Daniele Calandriello, Michal Valko:
Exact sampling of determinantal point processes with sublinear time preprocessing. CoRR abs/1905.13476 (2019) - [i11]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Bayesian experimental design using regularized determinantal point processes. CoRR abs/1906.04133 (2019) - [i10]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Unbiased estimators for random design regression. CoRR abs/1907.03411 (2019) - [i9]Mojmír Mutný, Michal Derezinski, Andreas Krause:
Convergence Analysis of the Randomized Newton Method with Determinantal Sampling. CoRR abs/1910.11561 (2019) - [i8]Michal Derezinski, Feynman T. Liang, Michael W. Mahoney:
Exact expressions for double descent and implicit regularization via surrogate random design. CoRR abs/1912.04533 (2019) - 2018
- [b1]Michal Derezinski:
Volume sampling for linear regression. University of California, Santa Cruz, USA, 2018 - [j1]Michal Derezinski, Manfred K. Warmuth:
Reverse Iterative Volume Sampling for Linear Regression. J. Mach. Learn. Res. 19: 23:1-23:39 (2018) - [c6]Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. AISTATS 2018: 716-725 - [c5]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. AISTATS 2018: 736-744 - [c4]Michal Derezinski
Discovering Surprising Documents with Context-Aware Word Representations. IUI 2018: 31-35 - [c3]Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu:
Leveraged volume sampling for linear regression. NeurIPS 2018: 2510-2519 - [i7]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Tail bounds for volume sampled linear regression. CoRR abs/1802.06749 (2018) - [i6]Michal Derezinski, Manfred K. Warmuth:
Reverse iterative volume sampling for linear regression. CoRR abs/1806.01969 (2018) - [i5]Michal Derezinski, Manfred K. Warmuth, Daniel Hsu:
Correcting the bias in least squares regression with volume-rescaled sampling. CoRR abs/1810.02453 (2018) - [i4]Michal Derezinski:
Fast determinantal point processes via distortion-free intermediate sampling. CoRR abs/1811.03717 (2018) - 2017
- [c2]Michal Derezinski, Manfred K. Warmuth:
Unbiased estimates for linear regression via volume sampling. NIPS 2017: 3084-3093 - [i3]Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. CoRR abs/1704.06731 (2017) - [i2]Michal Derezinski, Manfred K. Warmuth:
Unbiased estimates for linear regression via volume sampling. CoRR abs/1705.06908 (2017) - [i1]Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. CoRR abs/1710.05110 (2017) - 2014
- [c1]Michal Derezinski, Manfred K. Warmuth:
The limits of squared Euclidean distance regularization. NIPS 2014: 2807-2815
Coauthor Index
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last updated on 2024-10-07 22:18 CEST by the dblp team
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