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Debarghya Ghoshdastidar
Debarghya Ghoshdastidar
Email verificata su cit.tum.de - Home page
Titolo
Citata da
Citata da
Anno
Consistency of spectral hypergraph partitioning under planted partition model
D Ghoshdastidar, A Dukkipati
The Annals of Statistics 45 (1), 289-315, 2017
1032017
Consistency of spectral partitioning of uniform hypergraphs under planted partition model
D Ghoshdastidar, A Dukkipati
Advances in Neural Information Processing Systems 27, 2014
1002014
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
D Ghoshdastidar, M Gutzeit, A Carpentier, U von Luxburg
Annals of Statistics 48 (4), 2208-2229, 2020
612020
A provable generalized tensor spectral method for uniform hypergraph partitioning
D Ghoshdastidar, A Dukkipati
International Conference on Machine Learning, 400-409, 2015
552015
Uniform hypergraph partitioning: Provable tensor methods and sampling techniques
D Ghoshdastidar, A Dukkipati
Journal of Machine Learning Research 18 (50), 1-41, 2017
542017
Two-sample tests for large random graphs using network statistics
D Ghoshdastidar, M Gutzeit, A Carpentier, U von Luxburg
Conference on Learning Theory (COLT) 65, 954-977, 2017
532017
Practical methods for graph two-sample testing
D Ghoshdastidar, U Von Luxburg
Advances in Neural Information Processing Systems 31, 2018
472018
Comparison based nearest neighbor search
S Haghiri, D Ghoshdastidar, U von Luxburg
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2017
412017
Learning theory can (sometimes) explain generalisation in graph neural networks
P Esser, L Chennuru Vankadara, D Ghoshdastidar
Advances in Neural Information Processing Systems 34, 27043-27056, 2021
352021
Spectral clustering using multilinear SVD: Analysis, approximations and applications
D Ghoshdastidar, A Dukkipati
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
352015
Foundations of comparison-based hierarchical clustering
D Ghoshdastidar, M Perrot, U von Luxburg
Advances in neural information processing systems 32, 2019
322019
HOLISMOKES-VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning
S Huber, SH Suyu, D Ghoshdastidar, S Taubenberger, V Bonvin, ...
Astronomy & Astrophysics 658, A157, 2022
232022
Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions
D Ghoshdastidar, A Dukkipati, S Bhatnagar
ACM Transactions on Modeling and Computer Simulation (TOMACS) 24 (3), 1-26, 2014
132014
On the optimality of kernels for high-dimensional clustering
LC Vankadara, D Ghoshdastidar
International conference on artificial intelligence and statistics, 2185-2195, 2020
122020
Representation power of graph convolutions: Neural tangent kernel analysis
M Sabanayagam, P Esser, D Ghoshdastidar
92022
Mixture modeling with compact support distributions for unsupervised learning
A Dukkipati, D Ghoshdastidar, J Krishnan
2016 International Joint Conference on Neural Networks (IJCNN), 2706-2713, 2016
92016
q-Gaussian based smoothed functional algorithms for stochastic optimization
D Ghoshdastidar, A Dukkipati, S Bhatnagar
2012 IEEE International Symposium on Information Theory Proceedings, 1059-1063, 2012
92012
New insights into graph convolutional networks using neural tangent kernels
M Sabanayagam, P Esser, D Ghoshdastidar
arXiv preprint arXiv:2110.04060, 2021
82021
Causal forecasting: generalization bounds for autoregressive models
LC Vankadara, PM Faller, M Hardt, L Minorics, D Ghoshdastidar, ...
Uncertainty in Artificial Intelligence, 2002-2012, 2022
72022
Graphon based clustering and testing of networks: Algorithms and theory
M Sabanayagam, LC Vankadara, D Ghoshdastidar
arXiv preprint arXiv:2110.02722, 2021
72021
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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