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Leena Chennuru Vankadara
Leena Chennuru Vankadara
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
Measures of distortion for machine learning
L Chennuru Vankadara, U von Luxburg
Advances in Neural Information Processing Systems 31, 2018
72018
On the optimality of kernels for high-dimensional clustering
LC Vankadara, D Ghoshdastidar
International Conference on Artificial Intelligence and Statistics, 2185-2195, 2020
62020
A robotic home assistant with memory aid functionality
I Wieser, S Toprak, A Grenzing, T Hinz, S Auddy, EC Karaoğuz, ...
Joint German/Austrian Conference on Artificial Intelligence (Künstliche …, 2016
62016
Large scale representation learning from triplet comparisons
S Haghiri, LC Vankadara, U von Luxburg
22019
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
12021
Causal Forecasting: Generalization Bounds for Autoregressive Models
LC Vankadara, PM Faller, L Minorics, D Ghoshdastidar, D Janzing
arXiv preprint arXiv:2111.09831, 2021
12021
Recovery guarantees for kernel-based clustering under non-parametric mixture models
LC Vankadara, S Bordt, U von Luxburg, D Ghoshdastidar
International Conference on Artificial Intelligence and Statistics, 3817-3825, 2021
12021
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
LC Vankadara, S Haghiri, M Lohaus, FU Wahab, U von Luxburg
arXiv preprint arXiv:1912.01666, 2019
12019
Interpolation and Regularization for Causal Learning
LC Vankadara, L Rendsburg, U von Luxburg, D Ghoshdastidar
arXiv preprint arXiv:2202.09054, 2022
2022
Interpolation and Regularization for Causal Learning
L Chennuru Vankadara, L Rendsburg, U von Luxburg, D Ghoshdastidar
arXiv e-prints, arXiv: 2202.09054, 2022
2022
Graphon based Clustering and Testing of Networks: Algorithms and Theory
M Sabanayagam, LC Vankadara, D Ghoshdastidar
arXiv preprint arXiv:2110.02722, 2021
2021
Large scale ordinal embedding: training neural networks with structure-free inputs
LC Vankadara, S Haghiri, FU Wahab, U von Luxburg
2020
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Articles 1–12