Michal Derezinski

Elizaveta Rebrova

SIAM J. Math. Data Sci., March, 2024

Stochastic Newton Proximal Extragradient Method.

[BibT_eX]

[DOI]

Ruichen Jiang

Aryan Mokhtari

CoRR, 2024

Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning.

[BibT_eX]

[DOI]

Christopher Musco

Jiaming Yang

CoRR, 2024

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems.

[BibT_eX]

[DOI]

CoRR, 2024

Distributed Least Squares in Small Space via Sketching and Bias Reduction.

[BibT_eX]

[DOI]

Sachin Garg

Kevin Tan

CoRR, 2024

Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients.

[BibT_eX]

[DOI]

Sachin Garg

Albert S. Berahas

CoRR, 2024

HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2024

Solving Dense Linear Systems Faster Than via Preconditioning.

[BibT_eX]

[DOI]

Jiaming Yang

Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Optimal Embedding Dimension for Sparse Subspace Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Hessian averaging in stochastic Newton methods achieves superlinear convergence.

[BibT_eX]

[DOI]

Sen Na

Math. Program., 2023

Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems.

[BibT_eX]

[DOI]

CoRR, 2023

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software.

[BibT_eX]

[DOI]

CoRR, 2023

Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs.

[BibT_eX]

[DOI]

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Unbiased estimators for random design regression.

[BibT_eX]

[DOI]

Daniel Hsu

J. Mach. Learn. Res., 2022

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches.

[BibT_eX]

[DOI]

CoRR, 2022

Domain Sparsification of Discrete Distributions Using Entropic Independence.

[BibT_eX]

[DOI]

Proceedings of the 13th Innovations in Theoretical Computer Science Conference, 2022

2021

LocalNewton: Reducing Communication Bottleneck for Distributed Learning.

[BibT_eX]

[DOI]

CoRR, 2021

LocalNewton: Reducing communication rounds for distributed learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract).

[BibT_eX]

[DOI]

Rajiv Khanna

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Sparse sketches with small inversion bias.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

Query complexity of least absolute deviation regression via robust uniform convergence.

[BibT_eX]

[DOI]

Xue Chen

Proceedings of the Conference on Learning Theory, 2021

2020

Determinantal Point Processes in Randomized Numerical Linear Algebra.

[BibT_eX]

[DOI]

CoRR, 2020

Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method.

[BibT_eX]

[DOI]

Rajiv Khanna

CoRR, 2020

Exact expressions for double descent and implicit regularization via surrogate random design.

[BibT_eX]

[DOI]

Feynman T. Liang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Precise expressions for random projections: Low-rank approximation and randomized Newton.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method.

[BibT_eX]

[DOI]

Rajiv Khanna

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sampling from a k-DPP without looking at all items.

[BibT_eX]

[DOI]

Daniele Calandriello

Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases.

[BibT_eX]

[DOI]

Nima Anari

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling.

[BibT_eX]

[DOI]

Mojmir Mutny

Andreas Krause

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Bayesian experimental design using regularized determinantal point processes.

[BibT_eX]

[DOI]

Feynman T. Liang

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Convergence Analysis of the Randomized Newton Method with Determinantal Sampling.

[BibT_eX]

[DOI]

Mojmír Mutný

Andreas Krause

CoRR, 2019

Distributed estimation of the inverse Hessian by determinantal averaging.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exact sampling of determinantal point processes with sublinear time preprocessing.

[BibT_eX]

[DOI]

Daniele Calandriello

Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2019

Fast determinantal point processes via distortion-free intermediate sampling.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2019

Correcting the bias in least squares regression with volume-rescaled sampling.

[BibT_eX]

[DOI]

Daniel Hsu

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Volume sampling for linear regression.

[BibT_eX]

[DOI]

PhD thesis, 2018

Reverse Iterative Volume Sampling for Linear Regression.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2018

Tail bounds for volume sampled linear regression.

[BibT_eX]

[DOI]

Daniel Hsu

CoRR, 2018

Leveraged volume sampling for linear regression.

[BibT_eX]

[DOI]

Daniel J. Hsu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Discovering Surprising Documents with Context-Aware Word Representations.

[BibT_eX]

[DOI]

Khashayar Rohanimanesh

Aamer Hydrie

Proceedings of the 23rd International Conference on Intelligent User Interfaces, 2018

Subsampling for Ridge Regression via Regularized Volume Sampling.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Batch-Expansion Training: An Efficient Optimization Framework.

[BibT_eX]

[DOI]

Dhruv Mahajan

S. Sathiya Keerthi

S. V. N. Vishwanathan

Markus Weimer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning.

[BibT_eX]

[DOI]

Dhruv Mahajan

S. Sathiya Keerthi

S. V. N. Vishwanathan

Markus Weimer

CoRR, 2017

Unbiased estimates for linear regression via volume sampling.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2014

The limits of squared Euclidean distance regularization.

[BibT_eX]

[DOI]