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Michał Dereziński

  1. Determinantal Point Processes in Randomized Numerical Linear Algebra
    2020/05/07 by Michał Dereziński, Michael W. Mahoney, Dereziński, Michał +1 · 1 voice · 12 citations
    Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.DS #cs.LG
  2. Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software
    2023/02/22 by Riley Murray, James Demmel, Murray, Riley +23 · 19 citations
    Computer Science · Mathematics · #Stochastic Gradient Optimization Techniques #Tensor decomposition and applications #Parallel Computing and Optimization Techniques
  3. Unbiased estimates for linear regression via volume sampling
    2017/05/19 by Michał Dereziński, Dereziński, Michał, Manfred K. Warmuth +1 · 5 citations
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  4. Optimal Embedding Dimension for Sparse Subspace Embeddings
    2023/11/17 by Shabarish Chenakkod, Chenakkod, Shabarish, Michał Dereziński +5 · 5 citations
    Computer Science · Engineering · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Random Matrices and Applications #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  5. Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
    2022/08/20 by Michał Dereziński, Elizaveta Rebrova, Dereziński, Michał +1 · 4 citations
    Computer Science · Engineering · #60B20 #65F10 #68W20 #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Machine Learning and ELM #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  6. Exact sampling of determinantal point processes with sublinear time preprocessing
    2019/05/31 by Michał Dereziński, Dereziński, Michał, Daniele Calandriello +3 · 2 citations
    Computer Science · Mathematics · #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Random Matrices and Applications
  7. Exact expressions for double descent and implicit regularization via surrogate random design
    2019/12/10 by Michał Dereziński, Feynman Liang, Dereziński, Michał +3 · 3 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Random Matrices and Applications #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
  8. Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
    2024/05/09 by Michał Dereziński, Daniel LeJeune, Dereziński, Michał +5 · 5 citations
    Computer Science · Engineering · #Data Structures and Algorithms (cs.DS) #Digital Image Processing Techniques #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Matrix Theory and Algorithms #Numerical Analysis (math.NA) #Optimization and Control (math.OC)
  9. Solving Dense Linear Systems Faster Than via Preconditioning
    2023/12/14 by Michał Dereziński, Jiaming Yang, Dereziński, Michał +1 · 4 citations
    Computer Science · Engineering · Mathematics · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Markov Chains and Monte Carlo Methods #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  10. Unbiased estimators for random design regression
    2019/07/08 by Michał Dereziński, Manfred K. Warmuth, Dereziński, Michał +3 · 4 citations
    Computer Science · Environmental Science · #Bayesian Methods and Mixture Models #Computational Geometry and Mesh Generation #Soil Geostatistics and Mapping
  11. Distributed Least Squares in Small Space via Sketching and Bias Reduction
    2024/05/08 by Sachin Garg, Garg, Sachin, Kevin Yew Lee Tan +3 · 2 citations
    Computer Science · #Face and Expression Recognition
  12. Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
    2019/02/04 by Michał Dereziński, Dereziński, Michał, Kenneth L. Clarkson +5 · 1 citation
    Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
  13. Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
    2024/04/23 by Sachin Garg, Albert S. Berahas, Garg, Sachin +3 · 2 citations
    Computer Science · Engineering · #65K05 #90C06 #90C30 #Advanced Memory and Neural Computing #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Reservoir Computing #Optimization and Control (math.OC)
  14. Sparse sketches with small inversion bias
    2020/11/21 by Michał Dereziński, Dereziński, Michał, Zhenyu Liao +5 · 1 citation
    Engineering · Mathematics · Computer Science · #Sparse and Compressive Sensing Techniques #Random Matrices and Applications #Stochastic Gradient Optimization Techniques
  15. Precise expressions for random projections: Low-rank approximation and\n randomized Newton
    2020/06/18 by Michał Dereziński, Dereziński, Michał, Feynman Liang +5 · 1 citation
    Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  16. Randomized Kaczmarz Methods with Beyond-Krylov Convergence
    2025/01/20 by Michał Dereziński, Dereziński, Michał, Deanna Needell +5 · 3 citations
    Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Matrix Theory and Algorithms #Neural Networks and Applications #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
  17. Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
    2024/05/09 by Michał Dereziński, Christopher Musco, Dereziński, Michał +3 · 2 citations
    Computer Science · Physics and Astronomy · #Data Structures and Algorithms (cs.DS) #Electromagnetic Scattering and Analysis #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Matrix Theory and Algorithms #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Optimization and Control (math.OC)