Solving SDD linear systems in nearly mlog1/2n time. - Michael B. Cohen; Rasmus Kyng; Gary L. Miller; Jakub W. Pachocki; Richard Peng; Anup B. Rao; Shen Chen Xu (STOC, 2014)

Preconditioning in Expectation. - Michael B. Cohen; Rasmus Kyng; Jakub W. Pachocki; Richard Peng; Anup B. Rao (CoRR, 2014)

Algorithms for Lipschitz Learning on Graphs. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva; Daniel A. Spielman (COLT, 2015)

Fast, Provable Algorithms for Isotonic Regression in all L_p-norms. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva (NIPS, 2015)

Fast, Provable Algorithms for Isotonic Regression in all ℓp-norms. - Rasmus Kyng; Anup Rao 002; Sushant Sachdeva (CoRR, 2015)

Approximate Gaussian Elimination for Laplacians - Fast, Sparse, and Simple. - Rasmus Kyng; Sushant Sachdeva (FOCS, 2016)

Sparsified Cholesky and multigrid solvers for connection laplacians. - Rasmus Kyng; Yin Tat Lee; Richard Peng; Sushant Sachdeva; Daniel A. Spielman (STOC, 2016)

A Framework for Analyzing Resparsification Algorithms. - Rasmus Kyng; Jakub Pachocki; Richard Peng; Sushant Sachdeva (SODA, 2017)

Sampling random spanning trees faster than matrix multiplication. - David Durfee; Rasmus Kyng; John Peebles; Anup B. Rao; Sushant Sachdeva (STOC, 2017)

Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. - Michael B. Cohen; Jonathan A. Kelner; Rasmus Kyng; John Peebles; Richard Peng; Anup B. Rao; Aaron Sidford (FOCS, 2018)

A Matrix Chernoff Bound for Strongly Rayleigh Distributions and Spectral Sparsifiers from a few Random Spanning Trees. - Rasmus Kyng; Zhao Song 002 (FOCS, 2018)

Incomplete nested dissection. - Rasmus Kyng; Richard Peng; Robert Schwieterman; Peng Zhang 052 (STOC, 2018)

Iterative Refinement for ℓp-norm Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (SODA, 2019)

Flows in almost linear time via adaptive preconditioning. - Rasmus Kyng; Richard Peng; Sushant Sachdeva; Di Wang 005 (STOC, 2019)

Four Deviations Suffice for Rank 1 Matrices. - Rasmus Kyng; Kyle Luh; Zhao Song 002 (CoRR, 2019)

Iterative Refinement for 𝓁p-norm Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (CoRR, 2019)

Packing LPs are Hard to Solve Accurately, Assuming Linear Equations are Hard. - Rasmus Kyng; Di Wang 005; Peng Zhang 052 (SODA, 2020)

Hardness Results for Structured Linear Systems. - Rasmus Kyng; Peng Zhang 052 (SIAM J. Comput., 2020)

Almost-Linear-Time Weighted 𝓁p-Norm Solvers in Slightly Dense Graphs via Sparsification. - Deeksha Adil; Brian Bullins; Rasmus Kyng; Sushant Sachdeva (ICALP, 2021)

On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization. - Nicolas Emmenegger; Rasmus Kyng; Ahad N. Zehmakan (AISTATS, 2022)

Derandomizing Directed Random Walks in Almost-Linear Time. - Rasmus Kyng; Simon Meierhans; Maximilian Probst (FOCS, 2022)

Two-Commodity Flow Is Equivalent to Linear Programming Under Nearly-Linear Time Reductions. - Ming Ding 005; Rasmus Kyng; Peng Zhang 052 (ICALP, 2022)

Hardness Results for Laplacians of Simplicial Complexes via Sparse-Linear Equation Complete Gadgets. - Ming Ding 005; Rasmus Kyng; Maximilian Probst Gutenberg; Peng Zhang 052 (ICALP, 2022)

Faster Sparse Matrix Inversion and Rank Computation in Finite Fields. - Sílvia Casacuberta; Rasmus Kyng (ITCS, 2022)

Scalar and Matrix Chernoff Bounds from ℓ∞-Independence. - Tali Kaufman; Rasmus Kyng; Federico Soldà (SODA, 2022)

Incremental SSSP for Sparse Digraphs Beyond the Hopset Barrier. - Rasmus Kyng; Simon Meierhans; Maximilian Probst Gutenberg (SODA, 2022)

Fast Algorithms for 𝓁p-Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (CoRR, 2022)

A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. - Jan van den Brand; Li Chen 028; Richard Peng; Rasmus Kyng; Yang P. Liu; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (FOCS, 2023)

An Almost-Linear Time Algorithm for Maximum Flow and More (Invited Talk). - Rasmus Kyng (ICALP, 2023)

Maintaining Expander Decompositions via Sparse Cuts. - Yiding Hua; Rasmus Kyng; Maximilian Probst Gutenberg; Zihang Wu (SODA, 2023)

A Simple Framework for Finding Balanced Sparse Cuts via APSP. - Li Chen 028; Rasmus Kyng; Maximilian Probst Gutenberg; Sushant Sachdeva (SOSA, 2023)

Robust and Practical Solution of Laplacian Equations by Approximate Elimination. - Yuan Gao 025; Rasmus Kyng; Daniel A. Spielman (CoRR, 2023)

Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow. - Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva (Commun. ACM, 2023)

Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality. - Jan van den Brand; Li Chen 028; Rasmus Kyng; Yang P. Liu; Simon Meierhans; Maximilian Probst Gutenberg; Sushant Sachdeva (FOCS, 2024)

Optimal Electrical Oblivious Routing on Expanders. - Cella Florescu; Rasmus Kyng; Maximilian Probst Gutenberg; Sushant Sachdeva (ICALP, 2024)

Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. - Jan van den Brand; Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (SODA, 2024)

A Framework for Parallelizing Approximate Gaussian Elimination. - Yves Baumann; Rasmus Kyng (SPAA, 2024)

Almost-Linear Time Algorithms for Incremental Graphs: Cycle Detection, SCCs, s-t Shortest Path, and Minimum-Cost Flow. - Li Chen 028; Rasmus Kyng; Yang P. Liu; Simon Meierhans; Maximilian Probst Gutenberg (STOC, 2024)

A Dynamic Shortest Paths Toolbox: Low-Congestion Vertex Sparsifiers and Their Applications. - Rasmus Kyng; Simon Meierhans; Maximilian Probst Gutenberg (STOC, 2024)

Bootstrapping Dynamic APSP via Sparsification. - Rasmus Kyng; Simon Meierhans; Gernot Zöcklein (CoRR, 2024)

Fast Algorithms for ℓp-Regression. - Deeksha Adil; Rasmus Kyng; Richard Peng; Sushant Sachdeva (J. ACM, 2024)

Acceleration Meets Inverse Maintenance: Faster ℓ∞-Regression. - Deeksha Adil; Shunhua Jiang; Rasmus Kyng (ICALP, 2025)

A Simple Dynamic Spanner via APSP. - Rasmus Kyng; Simon Meierhans; Gernot Zöcklein (ICALP, 2025)

Almost-Linear Time Algorithms for Partially Dynamic Graphs (Invited Talk). - Rasmus Kyng (MFCS, 2025)

Assessing GPT Performance in a Proof-Based University-Level Course Under Blind Grading. - Ming Ding 005; Rasmus Kyng; Federico Soldà; Weixuan Yuan (CoRR, 2025)

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. - Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva (J. ACM, 2025)

Deterministic Almost-Linear-Time Gomory-Hu Trees. - Amir Abboud; Rasmus Kyng; Jason Li 006; Debmalya Panigrahi; Maximilian Probst Gutenberg; Thatchaphol Saranurak; Weixuan Yuan; Wuwei Yuan (CoRR, 2025)