Články s príkazom na verejný prístup - Hemant TyagiĎalšie informácie
Dostupné niekde: 19
SPONGE: A generalized eigenproblem for clustering signed networks
M Cucuringu, P Davies, A Glielmo, H Tyagi
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Príkazy: UK Engineering and Physical Sciences Research Council
Tangent space estimation for smooth embeddings of Riemannian manifolds ®
H Tyagi, E Vural, P Frossard
Information and Inference: A Journal of the IMA 2 (1), 69-114, 2013
Príkazy: Swiss National Science Foundation
Learning non-parametric basis independent models from point queries via low-rank methods
H Tyagi, V Cevher
Applied and Computational Harmonic Analysis 37 (3), 389-412, 2014
Príkazy: Swiss National Science Foundation
Sparse non-negative super-resolution—simplified and stabilised
A Eftekhari, J Tanner, A Thompson, B Toader, H Tyagi
Applied and Computational Harmonic Analysis 50, 216-280, 2021
Príkazy: UK Engineering and Physical Sciences Research Council
On denoising modulo 1 samples of a function
M Cucuringu, H Tyagi
International Conference on Artificial Intelligence and Statistics, 1868-1876, 2018
Príkazy: UK Engineering and Physical Sciences Research Council
Active learning of multi-index function models
T Hemant, V Cevher
Advances in Neural Information Processing Systems 25, 1475-1483, 2012
Príkazy: Swiss National Science Foundation
Ranking and synchronization from pairwise measurements via SVD
A d'Aspremont, M Cucuringu, H Tyagi
Journal of Machine Learning Research 22 (19), 1-63, 2021
Príkazy: UK Engineering and Physical Sciences Research Council
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
M Cucuringu, H Tyagi
Journal of Machine Learning Research 21 (32), 1-77, 2020
Príkazy: UK Engineering and Physical Sciences Research Council
Denoising modulo samples: k-NN regression and tightness of SDP relaxation
M Fanuel, H Tyagi
Information and Inference: A Journal of the IMA 11 (2), 637-677, 2022
Príkazy: Research Foundation (Flanders), European Commission
Learning sparse additive models with interactions in high dimensions
H Tyagi, A Kyrillidis, B Gärtner, A Krause
Artificial intelligence and statistics, 111-120, 2016
Príkazy: Swiss National Science Foundation
Efficient sampling for learning sparse additive models in high dimensions
H Tyagi, B Gärtner, A Krause
Advances in neural information processing systems 27, 2014
Príkazy: Swiss National Science Foundation
Learning ridge functions with randomized sampling in high dimensions
H Tyagi, V Cevher
2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012
Príkazy: Swiss National Science Foundation
Algorithms for learning sparse additive models with interactions in high dimensions*
H Tyagi, A Kyrillidis, B Gärtner, A Krause
Information and Inference: A Journal of the IMA 7 (2), 183-249, 2018
Príkazy: Swiss National Science Foundation, UK Engineering and Physical Sciences …
Multi-kernel unmixing and super-resolution using the Modified Matrix Pencil method
S Chrétien, H Tyagi
Journal of Fourier Analysis and Applications 26 (1), 18, 2020
Príkazy: UK Engineering and Physical Sciences Research Council
Learning general sparse additive models from point queries in high dimensions
H Tyagi, J Vybiral
Constructive Approximation 50 (3), 403-455, 2019
Príkazy: UK Engineering and Physical Sciences Research Council
Recovering Hölder smooth functions from noisy modulo samples
M Fanuel, H Tyagi
2021 55th Asilomar Conference on Signals, Systems, and Computers, 857-861, 2021
Príkazy: European Commission
Non-negative super-resolution is stable
A Eftekhari, J Tanner, A Thompson, B Toader, H Tyagi
2018 IEEE Data Science Workshop (DSW), 1-5, 2018
Príkazy: UK Engineering and Physical Sciences Research Council
Learning non-smooth sparse additive models from point queries in high dimensions
H Tyagi, J Vybiral
Príkazy: UK Engineering and Physical Sciences Research Council
Tangent space estimation bounds for smooth manifolds
H Tyagi, E Vural, P Frossard
Proceedings of SAMPTA, 2013
Príkazy: Swiss National Science Foundation
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