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Kiran Koshy Thekumparampil
Kiran Koshy Thekumparampil
Inne imiona/nazwiskaKiran Thekumparampil, Kiran K. Thekumparampil
Amazon, University of Illinois at Urbana-Champaign
Zweryfikowany adres z illinois.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Attention-based graph neural network for semi-supervised learning
KK Thekumparampil, C Wang, S Oh, LJ Li
arXiv preprint arXiv:1803.03735, 2018
4722018
Robustness of conditional gans to noisy labels
KK Thekumparampil, A Khetan, Z Lin, S Oh
Advances in neural information processing systems 31, 2018
3512018
Efficient algorithms for smooth minimax optimization
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in neural information processing systems 32, 2019
2362019
Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans
Z Lin, K Thekumparampil, G Fanti, S Oh
international conference on machine learning, 6127-6139, 2020
144*2020
Learning from comparisons and choices
S Negahban, S Oh, KK Thekumparampil, J Xu
Journal of Machine Learning Research 19 (40), 1-95, 2018
542018
Collaboratively learning preferences from ordinal data
S Oh, KK Thekumparampil, J Xu
Advances in Neural Information Processing Systems 28, 2015
382015
Lifted primal-dual method for bilinearly coupled smooth minimax optimization
KK Thekumparampil, N He, S Oh
International conference on artificial intelligence and statistics, 4281-4308, 2022
362022
Projection efficient subgradient method and optimal nonsmooth frank-wolfe method
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in neural information processing systems 33, 12211-12224, 2020
282020
Efficient algorithms for federated saddle point optimization
C Hou, KK Thekumparampil, G Fanti, S Oh
arXiv preprint arXiv:2102.06333, 2021
272021
Dpzero: Private fine-tuning of language models without backpropagation
L Zhang, B Li, KK Thekumparampil, S Oh, N He
arXiv preprint arXiv:2310.09639, 2023
26*2023
Bring your own algorithm for optimal differentially private stochastic minimax optimization
L Zhang, KK Thekumparampil, S Oh, N He
Advances in Neural Information Processing Systems 35, 35174-35187, 2022
242022
Sample efficient linear meta-learning by alternating minimization
KK Thekumparampil, P Jain, P Netrapalli, S Oh
arXiv preprint arXiv:2105.08306, 2021
182021
FeDChain: Chained algorithms for near-optimal communication cost in federated learning
C Hou, KK Thekumparampil, G Fanti, S Oh
arXiv preprint arXiv:2108.06869, 2021
152021
Combinatorial resource allocation using submodularity of waterfilling
K Thekumparampil, A Thangaraj, R Vaze
IEEE Transactions on Wireless Communications 15 (1), 206-216, 2015
152015
Statistically and computationally efficient linear meta-representation learning
KK Thekumparampil, P Jain, P Netrapalli, S Oh
Advances in Neural Information Processing Systems 34, 18487-18500, 2021
142021
Reducing the communication cost of federated learning through multistage optimization
C Hou, KK Thekumparampil, G Fanti, S Oh
CoRR, 2021
62021
Accelerating sinkhorn algorithm with sparse newton iterations
X Tang, M Shavlovsky, H Rahmanian, E Tardini, KK Thekumparampil, ...
arXiv preprint arXiv:2401.12253, 2024
52024
Robust conditional gans under missing or uncertain labels
KK Thekumparampil, S Oh, A Khetan
arXiv preprint arXiv:1906.03579, 2019
52019
Sub-modularity of waterfilling with applications to online basestation allocation
KK Thekumparampil, A Thangaraj, R Vaze
arXiv preprint arXiv:1402.4892, 2014
42014
A sinkhorn-type algorithm for constrained optimal transport
X Tang, H Rahmanian, M Shavlovsky, KK Thekumparampil, T Xiao, ...
arXiv preprint arXiv:2403.05054, 2024
32024
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