The limits of squared Euclidean distance regularization. - Michal Derezinski; Manfred K. Warmuth (NIPS, 2014)

Unbiased estimates for linear regression via volume sampling. - Michal Derezinski; Manfred K. Warmuth (NIPS, 2017)

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning. - Michal Derezinski; Dhruv Mahajan 001; S. Sathiya Keerthi; S. V. N. Vishwanathan; Markus Weimer (CoRR, 2017)

Batch-Expansion Training: An Efficient Optimization Framework. - Michal Derezinski; Dhruv Mahajan 001; S. Sathiya Keerthi; S. V. N. Vishwanathan; Markus Weimer (AISTATS, 2018)

Subsampling for Ridge Regression via Regularized Volume Sampling. - Michal Derezinski; Manfred K. Warmuth (AISTATS, 2018)

Discovering Surprising Documents with Context-Aware Word Representations. - Michal Derezinski; Khashayar Rohanimanesh; Aamer Hydrie (IUI, 2018)

Leveraged volume sampling for linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel J. Hsu (NeurIPS, 2018)

Tail bounds for volume sampled linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (CoRR, 2018)

Reverse Iterative Volume Sampling for Linear Regression. - Michal Derezinski; Manfred K. Warmuth (J. Mach. Learn. Res., 2018)

Volume sampling for linear regression. - Michal Derezinski (2018)

Correcting the bias in least squares regression with volume-rescaled sampling. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (AISTATS, 2019)

Fast determinantal point processes via distortion-free intermediate sampling. - Michal Derezinski (COLT, 2019)

Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression. - Michal Derezinski; Kenneth L. Clarkson; Michael W. Mahoney; Manfred K. Warmuth (COLT, 2019)

Exact sampling of determinantal point processes with sublinear time preprocessing. - Michal Derezinski; Daniele Calandriello; Michal Valko (NeurIPS, 2019)

Distributed estimation of the inverse Hessian by determinantal averaging. - Michal Derezinski; Michael W. Mahoney (NeurIPS, 2019)

Convergence Analysis of the Randomized Newton Method with Determinantal Sampling. - Mojmír Mutný; Michal Derezinski; Andreas Krause 001 (CoRR, 2019)

Bayesian experimental design using regularized determinantal point processes. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (AISTATS, 2020)

Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling. - Mojmir Mutny; Michal Derezinski; Andreas Krause 001 (AISTATS, 2020)

Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases. - Nima Anari; Michal Derezinski (FOCS, 2020)

Sampling from a k-DPP without looking at all items. - Daniele Calandriello; Michal Derezinski; Michal Valko (NeurIPS, 2020)

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization. - Michal Derezinski; Burak Bartan; Mert Pilanci; Michael W. Mahoney (NeurIPS, 2020)

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (NeurIPS, 2020)

Precise expressions for random projections: Low-rank approximation and randomized Newton. - Michal Derezinski; Feynman T. Liang; Zhenyu Liao 001; Michael W. Mahoney (NeurIPS, 2020)

Exact expressions for double descent and implicit regularization via surrogate random design. - Michal Derezinski; Feynman T. Liang; Michael W. Mahoney (NeurIPS, 2020)

Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method. - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Determinantal Point Processes in Randomized Numerical Linear Algebra. - Michal Derezinski; Michael W. Mahoney (CoRR, 2020)

Query complexity of least absolute deviation regression via robust uniform convergence. - Xue Chen; Michal Derezinski (COLT, 2021)

Sparse sketches with small inversion bias. - Michal Derezinski; Zhenyu Liao 001; Edgar Dobriban; Michael W. Mahoney (COLT, 2021)

Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). - Michal Derezinski; Rajiv Khanna; Michael W. Mahoney (IJCAI, 2021)

Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update. - Michal Derezinski; Jonathan Lacotte; Mert Pilanci; Michael W. Mahoney (NeurIPS, 2021)

LocalNewton: Reducing communication rounds for distributed learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (UAI, 2021)

LocalNewton: Reducing Communication Bottleneck for Distributed Learning. - Vipul Gupta; Avishek Ghosh; Michal Derezinski; Rajiv Khanna; Kannan Ramchandran; Michael W. Mahoney (CoRR, 2021)

Domain Sparsification of Discrete Distributions Using Entropic Independence. - Nima Anari; Michal Derezinski; Thuy-Duong Vuong; Elizabeth Yang (ITCS, 2022)

Unbiased estimators for random design regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (J. Mach. Learn. Res., 2022)

Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. - Michal Derezinski (COLT, 2023)

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. - Riley Murray; James Demmel; Michael W. Mahoney; N. Benjamin Erichson; Maksim Melnichenko; Osman Asif Malik; Laura Grigori; Piotr Luszczek; Michal Derezinski; Miles E. Lopes; Tianyu Liang; Hengrui Luo; et al. (CoRR, 2023)

Hessian averaging in stochastic Newton methods achieves superlinear convergence. - Sen Na; Michal Derezinski; Michael W. Mahoney (Math. Program., 2023)

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. - Michal Derezinski; Michael W. Mahoney (KDD, 2024)

Distributed Least Squares in Small Space via Sketching and Bias Reduction. - Sachin Garg; Kevin Tan; Michal Derezinski (NeurIPS, 2024)

Stochastic Newton Proximal Extragradient Method. - Ruichen Jiang; Michal Derezinski; Aryan Mokhtari (NeurIPS, 2024)

Optimal Embedding Dimension for Sparse Subspace Embeddings. - Shabarish Chenakkod; Michal Derezinski; Xiaoyu Dong; Mark Rudelson (STOC, 2024)

Solving Dense Linear Systems Faster Than via Preconditioning. - Michal Derezinski; Jiaming Yang (STOC, 2024)

HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks. - Yongyi Yang; Jiaming Yang; Wei Hu; Michal Derezinski (CoRR, 2024)

Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients. - Sachin Garg; Albert S. Berahas; Michal Derezinski (CoRR, 2024)

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems. - Michal Derezinski; Daniel LeJeune; Deanna Needell; Elizaveta Rebrova (CoRR, 2024)

Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition. - Michal Derezinski; Elizaveta Rebrova (SIAM J. Math. Data Sci., 2024)

Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method. - Sachin Garg; Michal Derezinski (COLT, 2025)

Optimal Oblivious Subspace Embeddings with Near-Optimal Sparsity. - Shabarish Chenakkod; Michal Derezinski; Xiaoyu Dong (ICALP, 2025)

Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning. - Michal Derezinski; Christopher Musco; Jiaming Yang (SODA, 2025)

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems. - Younghyun Cho; James Demmel; Michal Derezinski; Haoyun Li; Hengrui Luo; Michael W. Mahoney; Riley Murray (SIAM J. Matrix Anal. Appl., 2025)

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches. - Michal Derezinski (Trans. Mach. Learn. Res., 2025)

Randomized Kaczmarz Methods with Beyond-Krylov Convergence. - Michal Derezinski; Deanna Needell; Elizaveta Rebrova; Jiaming Yang (CoRR, 2025)

Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project. - Pratik Rathore; Zachary Frangella; Sachin Garg; Shaghayegh Fazliani; Michal Derezinski; Madeleine Udell (CoRR, 2025)

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure. - Michal Derezinski; Aaron Sidford (CoRR, 2025)

Optimal Subspace Embeddings: Resolving Nelson-Nguyen Conjecture Up to Sub-Polylogarithmic Factors. - Shabarish Chenakkod; Michal Derezinski; Xiaoyu Dong (CoRR, 2025)