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2020 – today
- 2024
[c11]Mehrdad Ghadiri
, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye:
Improving the Bit Complexity of Communication for Distributed Convex Optimization. STOC 2024: 1130-1140- [i12]Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, David P. Woodruff, Guanghao Ye:
Improving the Bit Complexity of Communication for Distributed Convex Optimization. CoRR abs/2403.19146 (2024) - [i11]Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra:
First-Order Methods for Linearly Constrained Bilevel Optimization. CoRR abs/2406.12771 (2024) - [i10]Guy Kornowski, Swati Padmanabhan, Ohad Shamir:
On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization. CoRR abs/2409.10323 (2024) - [i9]Daniel Pfrommer, Swati Padmanabhan, Kwangjun Ahn, Jack Umenberger, Tobia Marcucci, Zakaria Mhammedi, Ali Jadbabaie:
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control. CoRR abs/2410.00859 (2024) - 2023
- [c10]Swati Padmanabhan, David P. Woodruff, Richard Zhang:
Computing Approximate 𝓁p Sensitivities. NeurIPS 2023 - [c9]Zhe Feng, Swati Padmanabhan, Di Wang:
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers✱. WWW 2023: 3550-3560 - [i8]Swati Padmanabhan, David P. Woodruff, Qiuyi (Richard) Zhang:
Computing Approximate 𝓁p Sensitivities. CoRR abs/2311.04158 (2023) - 2022
- [c8]Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. NeurIPS 2022 - [c7]Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan, Chaobing Song:
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data. NeurIPS 2022 - [c6]Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. NeurIPS 2022 - [c5]Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford:
Computing Lewis Weights to High Precision. SODA 2022: 2723-2742 - [i7]Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan, Chaobing Song:
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data. CoRR abs/2203.03808 (2022) - [i6]Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity. CoRR abs/2208.03811 (2022) - [i5]Zhe Feng, Swati Padmanabhan, Di Wang:
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers. CoRR abs/2208.13713 (2022) - 2021
- [i4]Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford:
Computing Lewis Weights to High Precision. CoRR abs/2110.15563 (2021) - 2020
- [c4]Yin Tat Lee, Swati Padmanabhan:
An $\widetilde\mathcalO(m/\varepsilon^3.5)$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. COLT 2020: 3069-3119 - [c3]Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song:
A Faster Interior Point Method for Semidefinite Programming. FOCS 2020: 910-918 - [c2]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive semidefinite programming: mixed, parallel, and width-independent. STOC 2020: 789-802 - [i3]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR abs/2002.04830 (2020) - [i2]Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, Zhao Song:
A Faster Interior Point Method for Semidefinite Programming. CoRR abs/2009.10217 (2020)
2010 – 2019
- 2019
- [i1]Yin Tat Lee, Swati Padmanabhan:
An Õ(m/ε3.5)-Cost Algorithm for Semidefinite Programs with Diagonal Constraints. CoRR abs/1903.01859 (2019) - 2012
- [c1]Onur Mudanyali, Swati Padmanabhan, Stoyan Dimitrov, Isa Navruz, Uzair Sikora, Aydogan Ozcan:
Smart rapid diagnostics test reader running on a cell-phone for real-time mapping of epidemics. mHealthSys@SenSys 2012: 1:1-1:6
Coauthor Index
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last updated on 2024-11-06 21:34 CET by the dblp team
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