My broad interests lie in understanding the limits of learning by trying to answer questions such as when is learning possible and when is it not? Such questions are particularly interesting when posed in constrained settings, for instance, constraints on computational complexity or adversarial robustness. Understanding such fundamental limits foster the design of provably efficient and reliable Machine learning methods. More recently, I have been fascinated with statistical and causal properties of interpolating estimators in overparameterized model classes. You can find our recent work on this topic here. For a general overview of my work, please see my publications.
I joined Amazon Research as an Applied Scientist II, in January 2023. Previously, I was a Ph.D. candidate in the International Max Planck Research School for Intelligent Systems. I was jointly supervised by Prof.Debarghya Ghoshdastidar at the Theoretical Foundations of Artificial Intelligence, Technical University of Munich and Prof.Dr.Ulrike von Luxburg at the Theory of Machine learning group, University of Tuebingen.
Most recent publications on Google Scholar.
‡ indicates equal contribution.
A Consistent Estimator for Confounding Strength
Luca Rendsburg‡, Leena C Vankadara‡, Debarghya Ghosdastidar, Ulrike von Luxburg
A preprint: Arxiv'2022
Interpolation and Regularization for Causal Learning
Leena C Vankadara‡, Luca Rendsburg‡, Ulrike von Luxburg, Debarghya Ghoshdastidar
NeurIPS'22: Neural Information Processing Systems. 2022.
Causal Forecasting - Generalization Bounds for Autoregressive Models
Leena C Vankadara, Philipp Michael Faller, Mila Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
UAI'22: Uncertainity in Artificial Intelligence. 2022.
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Leena C Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
AISTATS'21: Artificial Intelligence and Statistics. 2021 (Oral presentation; 3% of all submissions)
On the optimality of kernels for high-dimensional clustering
Leena C Vankadara, Debarghya Ghoshdastidar
AISTATS'20: Artificial Intelligence and Statistics. 2020
Measures of distortion for machine learning
Leena C Vankadara, Ulrike von Luxburg
NeurIPS'18: Neural Information Processing Systems. 2018
A Consistent Estimator for Confounding Strength
Luca Rendsburg‡, Leena C Vankadara‡, Debarghya Ghosdastidar, Ulrike von Luxburg
A preprint: Arxiv'2022
Interpolation and Regularization for Causal Learning
Leena C Vankadara‡, Luca Rendsburg‡, Ulrike von Luxburg, Debarghya Ghoshdastidar
NeurIPS'22: Neural Information Processing Systems. 2022.
Causal Forecasting - Generalization Bounds for Autoregressive Models
Leena C Vankadara, Philipp Michael Faller, Mila Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
UAI'22: Uncertainity in Artificial Intelligence. 2022.
Graphon based Clustering and Testing of Networks - Algorithms and Theory
Mahalakshmi Sabanayagam, Leena C Vankadara, Debarghya Ghoshdastidar
ICLR'22: International Conference on Learning Representations. 2022
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Pascal Esser, Leena C Vankadara, Debarghya Ghoshdastidar
NeurIPS'21: Neural Information Processing Systems. 2021
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Leena C Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
AISTATS'21: Artificial Intelligence and Statistics. 2021 (Oral presentation; 3% of all submissions)
Insights into Ordinal Embedding Algorithms - A Systematic Evaluation
Leena C Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg
Arxiv. 2020
On the optimality of kernels for high-dimensional clustering
Leena C Vankadara, Debarghya Ghoshdastidar
AISTATS'20: Artificial Intelligence and Statistics. 2020
Measures of distortion for machine learning
Leena C Vankadara, Ulrike von Luxburg
NeurIPS'18: Neural Information Processing Systems. 2018
Full Resume in PDF.