Niki Kilbertus

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Giambattista ParascandoloOpenAIVerified email at openai.com
Mateo Rojas-CarullaLakera AIVerified email at lakera.ai
Matt J. KusnerUniversity College LondonVerified email at ucl.ac.uk
Fabian J TheisHelmholtz Munich, Technical University of MunichVerified email at helmholtz-muenchen.de
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Ricardo SilvaProfessor of Statistical Machine Learning and Data Science, Department of Statistical Science, UCLVerified email at stats.ucl.ac.uk
Moritz HardtMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Timothy GebhardPh.D. Student at the Max Planck ETH Center for Learning SystemsVerified email at tue.mpg.de
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Jiri HronGoogle DeepMindVerified email at google.com
Karl KrauthPostdoc, StanfordVerified email at stanford.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsVerified email at cs.uni-saarland.de
Manuel Gomez RodriguezFaculty, Max Planck Institute for Software Systems (MPI-SWS)Verified email at mpi-sws.org
Krishna P. GummadiHead, Networked Systems Group, MPI-SWSVerified email at mpi-sws.org
Michael VealeAssociate Professor in Digital Rights & Regulation, University College LondonVerified email at ucl.ac.uk
Adrià GascónGoogle ResearchVerified email at google.com
Paul CheslerMITVerified email at mit.edu

Niki Kilbertus

Technical University of Munich & Helmholtz Munich

Verified email at tum.de - Homepage

Machine Learning


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Avoiding discrimination through causal reasoning N Kilbertus, M Rojas Carulla, G Parascandolo, M Hardt, D Janzing, ... Advances in neural information processing systems, NeurIPS 30, 2017	755	2017
Learning Independent Causal Mechanisms G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf International Conference on Machine Learning, ICML 2018, 2018	194	2018
Blind Justice: Fairness with Encrypted Sensitive Attributes N Kilbertus, A Gascón, MJ Kusner, M Veale, KP Gummadi, A Weller International Conference on Machine Learning, ICML 2018, 2018	180	2018
Convolutional neural networks: A magic bullet for gravitational-wave detection? N Kilbertus, TD Gebhard, I Harry, B Schölkopf Physical Review D 100 (6), 063015, 2019	158*	2019
On disentangled representations learned from correlated data F Träuble, E Creager, N Kilbertus, A Goyal, F Locatello, B Schölkopf, ... International Conference on Machine Learning, ICML 2021, 2021	140	2021
Causal machine learning for predicting treatment outcomes S Feuerriegel, D Frauen, V Melnychuk, J Schweisthal, K Hess, A Curth, ... Nature Medicine 30 (4), 958-968, 2024	83	2024
Fair decisions despite imperfect predictions N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera Conference on Artificial Intelligence and Statistics, AISTATS 2019, 2019	71	2019
The sensitivity of counterfactual fairness to unmeasured confounding N Kilbertus, PJ Ball, MJ Kusner, A Weller, R Silva Conference on Uncertainty in Artificial Intelligence, UAI 2019, 2019	69	2019
Predicting single-cell perturbation responses for unseen drugs L Hetzel, S Böhm, N Kilbertus, S Günnemann, M Lotfollahi, F Theis Neural Information Processing Systems (NeurIPS) 2022, 2022	63*	2022
Generalization in anti-causal learning N Kilbertus, G Parascandolo, B Schölkopf NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018	63	2018
A Class of Algorithms for General Instrumental Variable Models N Kilbertus, MJ Kusner, R Silva Neural Information Processing Systems (NeurIPS) 2020, 2020	45	2020
Universal hydrodynamic flow in holographic planar shock collisions PM Chesler, N Kilbertus, W van der Schee Journal of High Energy Physics 2015 (11), 1-21, 2015	41	2015
Modeling content creator incentives on algorithm-curated platforms J Hron, K Krauth, MI Jordan, N Kilbertus, S Dean International Conference on Learning Representations (ICLR), 2023, 2022	40	2022
On component interactions in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus Neural Information Processing Systems (NeurIPS) 2021, 2021	37	2021
Multi-disciplinary fairness considerations in machine learning for clinical trials I Chien, N Deliu, R Turner, A Weller, S Villar, N Kilbertus Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022	29	2022
Stochastic Causal Programming for Bounding Treatment Effects K Padh, J Zeitler, D Watson, M Kusner, R Silva, N Kilbertus Conference on Causal Learning and Reasoning (CLeaR), 2023, 2022	25	2022
Quod erat knobelandum C Löh, S Krauss, N Kilbertus Springer Berlin Heidelberg, 2016	20	2016
Beyond predictions in neural odes: Identification and interventions H Aliee, FJ Theis, N Kilbertus arXiv preprint arXiv:2106.12430, 2021	19	2021
Odeformer: Symbolic regression of dynamical systems with transformers S d'Ascoli, S Becker, A Mathis, P Schwaller, N Kilbertus International Conference on Learning Representations, ICLR, 2024	18	2024
Predicting ordinary differential equations with transformers S Becker, M Klein, A Neitz, G Parascandolo, N Kilbertus International Conference on Machine Learning, ICML, 1978-2002, 2023	18*	2023

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