Leena Chennuru Vankadara

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Debarghya GhoshdastidarTechnical University of MunichVerified email at cit.tum.de
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Philipp M. FallerKarlsruhe Institute of TechnologyVerified email at partner.kit.edu
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Faiz Ul Wahabsynthavo GmbHVerified email at synthavo.de
Michael LohausUniversity of TübingenVerified email at uni-tuebingen.de
Pascal M. EsserTechnical University of MunichVerified email at in.tum.de
Luca RendsburgUniversity of TübingenVerified email at uni-tuebingen.de
Lenon MinoricsAmazon (AWS)Verified email at amazon.com
Mila HardtManaging Director at the medical data integration center at the University Hospital TübingenVerified email at uni-tuebingen.de
Francesco LocatelloAssistant Professor, ISTAVerified email at ista.ac.at
Mahalakshmi SabanayagamPhD, Technical University of MunichVerified email at tum.de
Atalanti A. MastakouriApplied Scientist, Amazon Web Services Research/ former Max Planck Institute for Intelligent SystemsVerified email at amazon.de
Sebastian BordtUniversity of TübingenVerified email at uni-tuebingen.de
Volkan CevherAssociate Professor, LIONS, EPFL. Amazon Scholar (AGI Foundations).Verified email at epfl.ch
Moritz HaasPhD Student, University of TübingenVerified email at uni-tuebingen.de
Maximilian FleissnerPhD at Technical University of MunichVerified email at tum.de
Jin XuPh.D. Student in Statistics, University of OxfordVerified email at stats.ox.ac.uk

Leena Chennuru Vankadara

Amazon Research; Previously University of Tübingen and Max Planck Institute, Tübingen

Verified email at uni-tuebingen.de - Homepage

Deep learning theory Causal learning theory High-dimensional statistics


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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	47	2021
Measures of distortion for machine learning L Chennuru Vankadara, U von Luxburg Advances in Neural Information Processing Systems 31, 2018	27	2018
A robotic home assistant with memory aid functionality I Wieser, S Toprak, A Grenzing, T Hinz, S Auddy, EC Karaoğuz, ... KI 2016: Advances in Artificial Intelligence: 39th Annual German Conference …, 2016	18	2016
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	14*	2019
On the optimality of kernels for high-dimensional clustering LC Vankadara, D Ghoshdastidar International conference on artificial intelligence and statistics, 2185-2195, 2020	12	2020
Self-compatibility: Evaluating causal discovery without ground truth PM Faller, LC Vankadara, AA Mastakouri, F Locatello, D Janzing International Conference on Artificial Intelligence and Statistics, 4132-4140, 2024	9	2024
Graphon based clustering and testing of networks: Algorithms and theory M Sabanayagam, LC Vankadara, D Ghoshdastidar arXiv preprint arXiv:2110.02722, 2021	9	2021
Causal forecasting: generalization bounds for autoregressive models LC Vankadara, PM Faller, M Hardt, L Minorics, D Ghoshdastidar, ... Uncertainty in Artificial Intelligence, 2002-2012, 2022	8	2022
A consistent estimator for confounding strength L Rendsburg, LC Vankadara, D Ghoshdastidar, U von Luxburg arXiv preprint arXiv:2211.01903, 2022	4	2022
Interpolation and regularization for causal learning L Chennuru Vankadara, L Rendsburg, U Luxburg, D Ghoshdastidar Advances in Neural Information Processing Systems 35, 36627-36639, 2022	3	2022
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	3	2021
Explaining Kernel Clustering via Decision Trees M Fleissner, LC Vankadara, D Ghoshdastidar arXiv preprint arXiv:2402.09881, 2024	2	2024
Reinterpreting causal discovery as the task of predicting unobserved joint statistics D Janzing, PM Faller, LC Vankadara arXiv preprint arXiv:2305.06894, 2023	2	2023
: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling M Haas, J Xu, V Cevher, LC Vankadara arXiv preprint arXiv:2411.00075, 2024		2024
On Feature Learning in Structured State Space Models LC Vankadara, J Xu, M Haas, V Cevher The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0

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