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ORCIDArun Jambulapati
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2020 – today
- 2024
[j1]Jose H. Blanchet
, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards optimal running timesfor optimal transport. Oper. Res. Lett. 52: 107054 (2024)- [c27]Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian:
Black-Box k-to-1-PCA Reductions: Theory and Applications. COLT 2024: 2564-2607 - [c26]Arun Jambulapati, Aaron Sidford, Kevin Tian:
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. COLT 2024: 2608-2643 - [c25]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. SODA 2024: 3685-3723 - [c24]Arun Jambulapati, Victor Reis, Kevin Tian:
Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory. SODA 2024: 5169-5208 - [c23]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Generalized Linear Models. STOC 2024: 1665-1675 - [i31]Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian:
Black-Box k-to-1-PCA Reductions: Theory and Applications. CoRR abs/2403.03905 (2024) - [i30]Arun Jambulapati, Aaron Sidford, Kevin Tian:
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. CoRR abs/2406.07373 (2024) - [i29]Arun Jambulapati, Sushant Sachdeva, Aaron Sidford, Kevin Tian, Yibin Zhao:
Eulerian Graph Sparsification by Effective Resistance Decomposition. CoRR abs/2408.10172 (2024) - 2023
- [c22]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Sums of Norms. FOCS 2023: 1953-1962 - [c21]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS 2023: 2031-2058 - [c20]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. NeurIPS 2023 - [c19]Arun Jambulapati, Kevin Tian:
Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations. NeurIPS 2023 - [c18]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. STOC 2023: 196-206 - [i28]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR abs/2301.00457 (2023) - [i27]Arun Jambulapati, Hilaf Hasson, Youngsuk Park, Yuyang Wang:
Testing Causality for High Dimensional Data. CoRR abs/2303.07774 (2023) - [i26]Arun Jambulapati, Victor Reis, Kevin Tian:
Linear-Sized Sparsifiers via Near-Linear Time Discrepancy Theory. CoRR abs/2305.08434 (2023) - [i25]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Sums of Norms. CoRR abs/2305.09049 (2023) - [i24]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. CoRR abs/2310.18265 (2023) - [i23]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. CoRR abs/2311.10886 (2023) - [i22]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying generalized linear models. CoRR abs/2311.18145 (2023) - 2022
- [c17]Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. ICALP 2022: 77:1-77:20 - [c16]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. ICML 2022: 2658-2685 - [c15]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. NeurIPS 2022 - [c14]Sepehr Assadi, Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space. SODA 2022: 627-669 - [c13]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Improved iteration complexities for overconstrained p-norm regression. STOC 2022: 529-542 - [c12]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster maxflow via improved dynamic spectral vertex sparsifiers. STOC 2022: 543-556 - [i21]Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. CoRR abs/2204.12721 (2022) - [i20]Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva:
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. CoRR abs/2205.06167 (2022) - [i19]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. CoRR abs/2205.15371 (2022) - [i18]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. CoRR abs/2206.08627 (2022) - [i17]Arun Jambulapati, Yin Tat Lee, Santosh S. Vempala:
A Slightly Improved Bound for the KLS Constant. CoRR abs/2208.11644 (2022) - [i16]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. CoRR abs/2209.10539 (2022) - 2021
- [c11]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. COLT 2021: 866-882 - [c10]Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian:
Robust Regression Revisited: Acceleration and Improved Estimation Rates. NeurIPS 2021: 4475-4488 - [c9]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. NeurIPS 2021: 10810-10822 - [c8]Arun Jambulapati, Aaron Sidford:
Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. SODA 2021: 540-559 - [i15]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. CoRR abs/2105.01778 (2021) - [i14]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. CoRR abs/2106.09481 (2021) - [i13]Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian:
Robust Regression Revisited: Acceleration and Improved Estimation Rates. CoRR abs/2106.11938 (2021) - [i12]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Improved Iteration Complexities for Overconstrained p-Norm Regression. CoRR abs/2111.01848 (2021) - [i11]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers. CoRR abs/2112.00722 (2021) - 2020
- [c7]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. NeurIPS 2020 - [c6]Arun Jambulapati, Jerry Li, Kevin Tian:
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. NeurIPS 2020 - [c5]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive semidefinite programming: mixed, parallel, and width-independent. STOC 2020: 789-802 - [i10]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR abs/2002.04830 (2020) - [i9]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. CoRR abs/2003.08078 (2020) - [i8]Arun Jambulapati, Jerry Li, Kevin Tian:
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. CoRR abs/2006.06980 (2020) - [i7]Arun Jambulapati, Aaron Sidford:
Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. CoRR abs/2011.08806 (2020)
2010 – 2019
- 2019
- [c4]Yang P. Liu, Arun Jambulapati, Aaron Sidford:
Parallel Reachability in Almost Linear Work and Square Root Depth. FOCS 2019: 1664-1686 - [c3]Arun Jambulapati, Aaron Sidford, Kevin Tian:
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. NeurIPS 2019: 11355-11366 - [c2]AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford:
Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. SODA 2019: 1387-1404 - [i6]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Parallel Reachability in Almost Linear Work and Square Root Depth. CoRR abs/1905.08841 (2019) - [i5]Arun Jambulapati, Aaron Sidford, Kevin Tian:
A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. CoRR abs/1906.00618 (2019) - 2018
- [c1]Arun Jambulapati, Aaron Sidford:
Efficient Õ(n/∊) Spectral Sketches for the Laplacian and its Pseudoinverse. SODA 2018: 2487-2503 - [i4]AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford:
Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. CoRR abs/1810.02348 (2018) - [i3]Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards Optimal Running Times for Optimal Transport. CoRR abs/1810.07717 (2018) - [i2]Arun Jambulapati, Kirankumar Shiragur, Aaron Sidford:
Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. CoRR abs/1812.06295 (2018) - 2017
- [i1]Arun Jambulapati, Aaron Sidford:
Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. CoRR abs/1711.00571 (2017)
Coauthor Index
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last updated on 2024-10-02 21:42 CEST by the dblp team
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