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ORCIDKiran Koshy Thekumparampil
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2020 – today
- 2024
[c11]Xun Tang, Michael Shavlovsky, Holakou Rahmanian, Elisa Tardini, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying:
Accelerating Sinkhorn algorithm with sparse Newton iterations. ICLR 2024- [c10]Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
DPZero: Private Fine-Tuning of Language Models without Backpropagation. ICML 2024 - [i18]Xun Tang, Michael Shavlovsky, Holakou Rahmanian, Elisa Tardini, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying:
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations. CoRR abs/2401.12253 (2024) - [i17]Xun Tang, Holakou Rahmanian, Michael Shavlovsky, Kiran Koshy Thekumparampil, Tesi Xiao, Lexing Ying:
A Sinkhorn-type Algorithm for Constrained Optimal Transport. CoRR abs/2403.05054 (2024) - 2023
- [i16]Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky, Giulia Fanti, Yesh Dattatreya, Sujay Sanghavi:
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity. CoRR abs/2308.00177 (2023) - [i15]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. CoRR abs/2310.09639 (2023) - 2022
- [c9]Kiran Koshy Thekumparampil, Niao He, Sewoong Oh:
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. AISTATS 2022: 4281-4308 - [c8]Charlie Hou, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning. ICLR 2022 - [c7]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. NeurIPS 2022 - [i14]Kiran Koshy Thekumparampil, Niao He, Sewoong Oh:
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. CoRR abs/2201.07427 (2022) - [i13]Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He:
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. CoRR abs/2206.00363 (2022) - 2021
- [c6]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS 2021: 18487-18500 - [i12]Charlie Hou, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
Efficient Algorithms for Federated Saddle Point Optimization. CoRR abs/2102.06333 (2021) - [i11]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR abs/2105.08306 (2021) - [i10]Charlie Hou, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
Reducing the Communication Cost of Federated Learning through Multistage Optimization. CoRR abs/2108.06869 (2021) - 2020
- [c5]Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs. ICML 2020: 6127-6139 - [c4]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS 2020 - [i9]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR abs/2010.01848 (2020)
2010 – 2019
- 2019
- [c3]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS 2019: 12659-12670 - [i8]Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan:
Robust conditional GANs under missing or uncertain labels. CoRR abs/1906.03579 (2019) - [i7]Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers. CoRR abs/1906.06034 (2019) - [i6]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. CoRR abs/1907.01543 (2019) - 2018
- [j2]Sahand Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Learning from Comparisons and Choices. J. Mach. Learn. Res. 19: 40:1-40:95 (2018) - [c2]Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh:
Robustness of conditional GANs to noisy labels. NeurIPS 2018: 10292-10303 - [i5]Kiran Koshy Thekumparampil, Chong Wang, Sewoong Oh, Li-Jia Li:
Attention-based Graph Neural Network for Semi-supervised Learning. CoRR abs/1803.03735 (2018) - [i4]Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh:
Robustness of Conditional GANs to Noisy Labels. CoRR abs/1811.03205 (2018) - 2017
- [i3]Sahand Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Learning from Comparisons and Choices. CoRR abs/1704.07228 (2017) - 2016
- [j1]Kiran Koshy Thekumparampil, Andrew Thangaraj, Rahul Vaze:
Combinatorial Resource Allocation Using Submodularity of Waterfilling. IEEE Trans. Wirel. Commun. 15(1): 206-216 (2016) - 2015
- [c1]Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Collaboratively Learning Preferences from Ordinal Data. NIPS 2015: 1909-1917 - [i2]Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu:
Collaboratively Learning Preferences from Ordinal Data. CoRR abs/1506.07947 (2015) - 2014
- [i1]Kiran Koshy Thekumparampil, Andrew Thangaraj, Rahul Vaze:
Sub-Modularity of Waterfilling with Applications to Online Basestation Allocation. CoRR abs/1402.4892 (2014)
Coauthor Index
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