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; et al. (KI, 2016)

Measures of distortion for machine learning. - Leena Chennuru Vankadara; Ulrike von Luxburg (NeurIPS, 2018)

Large scale representation learning from triplet comparisons. - Siavash Haghiri; Leena Chennuru Vankadara; Ulrike von Luxburg (CoRR, 2019)

On the optimality of kernels for high-dimensional clustering. - Leena Chennuru Vankadara; Debarghya Ghoshdastidar (AISTATS, 2020)

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. - Leena C. Vankadara; Sebastian Bordt; Ulrike von Luxburg; Debarghya Ghoshdastidar (AISTATS, 2021)

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. - Pascal Mattia Esser; Leena C. Vankadara; Debarghya Ghoshdastidar (NeurIPS, 2021)

Causal Forecasting: Generalization Bounds for Autoregressive Models. - Leena C. Vankadara; Philipp Michael Faller; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (CoRR, 2021)

Graphon based Clustering and Testing of Networks: Algorithms and Theory. - Mahalakshmi Sabanayagam; Leena Chennuru Vankadara; Debarghya Ghoshdastidar (ICLR, 2022)

Interpolation and Regularization for Causal Learning. - Leena Chennuru Vankadara; Luca Rendsburg; Ulrike von Luxburg; Debarghya Ghoshdastidar (NeurIPS, 2022)

Causal forecasting: generalization bounds for autoregressive models. - Leena Chennuru Vankadara; Philipp Michael Faller; Michaela Hardt; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (UAI, 2022)

A Consistent Estimator for Confounding Strength. - Luca Rendsburg; Leena Chennuru Vankadara; Debarghya Ghoshdastidar; Ulrike von Luxburg (CoRR, 2022)

Reinterpreting causal discovery as the task of predicting unobserved joint statistics. - Dominik Janzing; Philipp Michael Faller; Leena Chennuru Vankadara (CoRR, 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)

Self-Compatibility: Evaluating Causal Discovery without Ground Truth. - Philipp Michael Faller; Leena C. Vankadara; Atalanti-Anastasia Mastakouri; Francesco Locatello; Dominik Janzing (AISTATS, 2024)

Explaining Kernel Clustering via Decision Trees. - Maximilian Fleissner; Leena Chennuru Vankadara; Debarghya Ghoshdastidar (ICLR, 2024)

μP2: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling. - Moritz Haas; Jin Xu; Volkan Cevher; Leena Chennuru Vankadara (NeurIPS, 2024)

On Feature Learning in Structured State Space Models. - Leena Chennuru Vankadara; Jin Xu; Moritz Haas; Volkan Cevher (NeurIPS, 2024)

μ P2: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling. - Moritz Haas; Jin Xu; Volkan Cevher; Leena Chennuru Vankadara (CoRR, 2024)

On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling. - Moritz Haas; Sebastian Bordt; Ulrike von Luxburg; Leena Chennuru Vankadara (CoRR, 2025)