Leena C. Vankadara

According to our database, Leena C. Vankadara authored at least 15 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Explaining Kernel Clustering via Decision Trees.

Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Self-Compatibility: Evaluating Causal Discovery without Ground Truth.

Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Insights into Ordinal Embedding Algorithms: A Systematic Evaluation.

Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg

J. Mach. Learn. Res., 2023

Reinterpreting causal discovery as the task of predicting unobserved joint statistics.

Dominik Janzing, Philipp Michael Faller, Leena Chennuru Vankadara

CoRR, 2023

2022

A Consistent Estimator for Confounding Strength.

Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg

CoRR, 2022

Causal forecasting: generalization bounds for autoregressive models.

Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Interpolation and Regularization for Causal Learning.

Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graphon based Clustering and Testing of Networks: Algorithms and Theory.

Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Causal Forecasting: Generalization Bounds for Autoregressive Models.

Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

CoRR, 2021

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks.

Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models.

Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the optimality of kernels for high-dimensional clustering.

Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Large scale representation learning from triplet comparisons.

Siavash Haghiri, Leena Chennuru Vankadara, Ulrike von Luxburg

CoRR, 2019

2018

Measures of distortion for machine learning.

Leena Chennuru Vankadara, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

A Robotic Home Assistant with Memory Aid Functionality.

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

Proceedings of the KI 2016: Advances in Artificial Intelligence, 2016