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Leena C. Vankadara
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- affiliation: University of Tübingen, Department of Computer Science, Germany
- affiliation: Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany
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2020 – today
- 2024
- [c10]Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. AISTATS 2024: 4132-4140 - [c9]Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Explaining Kernel Clustering via Decision Trees. ICLR 2024 - [i12]Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Explaining Kernel Clustering via Decision Trees. CoRR abs/2402.09881 (2024) - [i11]Moritz Haas, Jin Xu, Volkan Cevher, Leena Chennuru Vankadara:
μ P2: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling. CoRR abs/2411.00075 (2024) - 2023
- [j1]Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg:
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. J. Mach. Learn. Res. 24: 191:1-191:83 (2023) - [i10]Dominik Janzing, Philipp Michael Faller, Leena Chennuru Vankadara:
Reinterpreting causal discovery as the task of predicting unobserved joint statistics. CoRR abs/2305.06894 (2023) - [i9]Philipp Michael Faller, Leena Chennuru Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. CoRR abs/2307.09552 (2023) - 2022
- [c8]Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Graphon based Clustering and Testing of Networks: Algorithms and Theory. ICLR 2022 - [c7]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. NeurIPS 2022 - [c6]Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal forecasting: generalization bounds for autoregressive models. UAI 2022: 2002-2012 - [i8]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. CoRR abs/2202.09054 (2022) - [i7]Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg:
A Consistent Estimator for Confounding Strength. CoRR abs/2211.01903 (2022) - 2021
- [c5]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS 2021: 3817-3825 - [c4]Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar:
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. NeurIPS 2021: 27043-27056 - [i6]Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Graphon based Clustering and Testing of Networks: Algorithms and Theory. CoRR abs/2110.02722 (2021) - [i5]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR abs/2110.09476 (2021) - [i4]Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR abs/2111.09831 (2021) - [i3]Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar:
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. CoRR abs/2112.03968 (2021) - 2020
- [c3]Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
On the optimality of kernels for high-dimensional clustering. AISTATS 2020: 2185-2195
2010 – 2019
- 2019
- [i2]Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
On the optimality of kernels for high-dimensional clustering. CoRR abs/1912.00458 (2019) - [i1]Siavash Haghiri, Leena Chennuru Vankadara, Ulrike von Luxburg:
Large scale representation learning from triplet comparisons. CoRR abs/1912.01666 (2019) - 2018
- [c2]Leena Chennuru Vankadara, Ulrike von Luxburg:
Measures of distortion for machine learning. NeurIPS 2018: 4891-4900 - 2016
- [c1]Iris Wieser, Sibel Toprak, Andreas Grenzing, Tobias Hinz, Sayantan Auddy, Ethem Can Karaoguz, Abhilash Chandran, Melanie Remmels, Ahmed El Shinawi, Josip Josifovski, Leena Chennuru Vankadara, Faiz Ul Wahab, Alireza MollaAlizadeh Bahnemiri, Debasish Sahu, Stefan Heinrich, Nicolás Navarro-Guerrero, Erik Strahl, Johannes Twiefel, Stefan Wermter:
A Robotic Home Assistant with Memory Aid Functionality. KI 2016: 102-115
Coauthor Index
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last updated on 2024-12-11 20:43 CET by the dblp team
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