| 2026 | COLT | Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size. | Michal Derezinski, Xiaoyu Dong |
| 2026 | COLT | The matrix-vector complexity of Ax=b. | Michal Derezinski, Ethan N. Epperly, Raphael A. Meyer |
| 2026 | SODA | Optimal Subspace Embeddings: Resolving Nelson-Nguyen Conjecture Up to Sub-Polylogarithmic Factors. | Shabarish Chenakkod, Michal Derezinski, Xiaoyu Dong |
| 2026 | SODA | Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure. | Michal Derezinski, Aaron Sidford |
| 2025 | COLT | Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nystrm Method. | Sachin Garg, Michal Derezinski |
| 2025 | ICALP | Optimal Oblivious Subspace Embeddings with Near-Optimal Sparsity. | Shabarish Chenakkod, Michal Derezinski, Xiaoyu Dong |
| 2025 | SODA | Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning. | Michal Derezinski, Christopher Musco, Jiaming Yang |
| 2024 | KDD | Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning. | Michal Derezinski, Michael W. Mahoney |
| 2024 | STOC | Optimal Embedding Dimension for Sparse Subspace Embeddings. | Shabarish Chenakkod, Michal Derezinski, Xiaoyu Dong, Mark Rudelson |
| 2024 | STOC | Solving Dense Linear Systems Faster Than via Preconditioning. | Michal Derezinski, Jiaming Yang |
| 2023 | COLT | Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. | Michal Derezinski |
| 2021 | COLT | Query complexity of least absolute deviation regression via robust uniform convergence. | Xue Chen, Michal Derezinski |
| 2021 | COLT | Sparse sketches with small inversion bias. | Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney |
| 2021 | IJCAI | Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract). | Michal Derezinski, Rajiv Khanna, Michael W. Mahoney |
| 2021 | UAI | LocalNewton: Reducing communication rounds for distributed learning. | Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney |
| 2020 | AISTATS | Bayesian experimental design using regularized determinantal point processes. | Michal Derezinski, Feynman T. Liang, Michael W. Mahoney |
| 2020 | AISTATS | Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling. | Mojmir Mutny, Michal Derezinski, Andreas Krause |
| 2020 | FOCS | Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases. | Nima Anari, Michal Derezinski |
| 2019 | AISTATS | Correcting the bias in least squares regression with volume-rescaled sampling. | Michal Derezinski, Manfred K. Warmuth, Daniel Hsu |
| 2019 | COLT | Fast determinantal point processes via distortion-free intermediate sampling. | Michal Derezinski |
| 2019 | COLT | 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 |
| 2018 | AISTATS | Batch-Expansion Training: An Efficient Optimization Framework. | Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer |
| 2018 | AISTATS | Subsampling for Ridge Regression via Regularized Volume Sampling. | Michal Derezinski, Manfred K. Warmuth |
| 2018 | IUI | Discovering Surprising Documents with Context-Aware Word Representations. | Michal Derezinski, Khashayar Rohanimanesh, Aamer Hydrie |