Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. - Arun Jambulapati; Aaron Sidford (CoRR, 2017)

Efficient Õ(n/∊) Spectral Sketches for the Laplacian and its Pseudoinverse. - Arun Jambulapati; Aaron Sidford (SODA, 2018)

Towards Optimal Running Times for Optimal Transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (CoRR, 2018)

Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. - Arun Jambulapati; Kirankumar Shiragur; Aaron Sidford (CoRR, 2018)

Parallel Reachability in Almost Linear Work and Square Root Depth. - Yang P. Liu; Arun Jambulapati; Aaron Sidford (FOCS, 2019)

A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. - AmirMahdi Ahmadinejad; Arun Jambulapati; Amin Saberi; Aaron Sidford (SODA, 2019)

A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (CoRR, 2019)

Acceleration with a Ball Optimization Oracle. - Yair Carmon; Arun Jambulapati; Qijia Jiang; Yujia Jin; Yin Tat Lee; Aaron Sidford; Kevin Tian (NeurIPS, 2020)

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. - Arun Jambulapati; Jerry Li 001; Kevin Tian (NeurIPS, 2020)

Positive semidefinite programming: mixed, parallel, and width-independent. - Arun Jambulapati; Yin Tat Lee; Jerry Li 001; Swati Padmanabhan; Kevin Tian (STOC, 2020)

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (COLT, 2021)

Stochastic Bias-Reduced Gradient Methods. - Hilal Asi; Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2021)

Robust Regression Revisited: Acceleration and Improved Estimation Rates. - Arun Jambulapati; Jerry Li 001; Tselil Schramm; Kevin Tian (NeurIPS, 2021)

Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. - Arun Jambulapati; Aaron Sidford (SODA, 2021)

Improved Iteration Complexities for Overconstrained p-Norm Regression. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (CoRR, 2021)

Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. - Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (ICALP, 2022)

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (ICML, 2022)

Optimal and Adaptive Monteiro-Svaiter Acceleration. - Yair Carmon; Danielle Hausler; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2022)

Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space. - Sepehr Assadi; Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (SODA, 2022)

Faster maxflow via improved dynamic spectral vertex sparsifiers. - Jan van den Brand; Yu Gao 001; Arun Jambulapati; Yin Tat Lee; Yang P. Liu; Richard Peng; Aaron Sidford (STOC, 2022)

Improved iteration complexities for overconstrained p-norm regression. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (STOC, 2022)

Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Sushant Sachdeva (CoRR, 2022)

A Slightly Improved Bound for the KLS Constant. - Arun Jambulapati; Yin Tat Lee; Santosh S. Vempala (CoRR, 2022)

ReSQueing Parallel and Private Stochastic Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Yin Tat Lee; Daogao Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Sparsifying Sums of Norms. - Arun Jambulapati; James R. Lee; Yang P. Liu; Aaron Sidford (FOCS, 2023)

Structured Semidefinite Programming for Recovering Structured Preconditioners. - Arun Jambulapati; Jerry Li 001; Christopher Musco; Kirankumar Shiragur; Aaron Sidford; Kevin Tian (NeurIPS, 2023)

Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations. - Arun Jambulapati; Kevin Tian (NeurIPS, 2023)

Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (STOC, 2023)

Testing Causality for High Dimensional Data. - Arun Jambulapati; Hilaf Hasson; Youngsuk Park; Yuyang Wang 001 (CoRR, 2023)

Black-Box k-to-1-PCA Reductions: Theory and Applications. - Arun Jambulapati; Syamantak Kumar; Jerry Li 001; Shourya Pandey; Ankit Pensia; Kevin Tian (COLT, 2024)

Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. - Arun Jambulapati; Aaron Sidford; Kevin Tian (COLT, 2024)

Testing Calibration in Nearly-Linear Time. - Lunjia Hu; Arun Jambulapati; Kevin Tian; Chutong Yang (NeurIPS, 2024)

A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (SODA, 2024)

Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory. - Arun Jambulapati; Victor Reis; Kevin Tian (SODA, 2024)

Sparsifying Generalized Linear Models. - Arun Jambulapati; James R. Lee; Yang P. Liu; Aaron Sidford (STOC, 2024)

Convex optimization with p-norm oracles. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Aaron Sidford (CoRR, 2024)

Towards optimal running timesfor optimal transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (Oper. Res. Lett., 2024)

Extracting Dual Solutions via Primal Optimizers. - Yair Carmon; Arun Jambulapati; Liam O'Carroll; Aaron Sidford (ITCS, 2025)

Eulerian Graph Sparsification by Effective Resistance Decomposition. - Arun Jambulapati; Sushant Sachdeva; Aaron Sidford; Kevin Tian; Yibin Zhao 003 (SODA, 2025)

Radial Isotropic Position via an Implicit Newton's Method. - Arun Jambulapati; Jonathan Li 003; Kevin Tian (CoRR, 2025)