Niki Kilbertus

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichVerified email at tuebingen.mpg.de
Giambattista ParascandoloOpenAIVerified email at openai.com
Matt J. KusnerAssociate Professor, University College LondonVerified email at ucl.ac.uk
Mateo Rojas-CarullaLakera AIVerified email at lakera.ai
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Moritz HardtMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Ricardo SilvaProfessor of Statistical Machine Learning and Data Science, Department of Statistical Science, UCLVerified email at stats.ucl.ac.uk
Timothy GebhardPhD Student at the Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Fabian J TheisHelmholtz Munich, Technical University of MunichVerified email at helmholtz-muenchen.de
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
Jiri HronPhD student, University of CambridgeVerified email at cam.ac.uk
Karl KrauthPhD student, UC BerkeleyVerified email at berkeley.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Paul CheslerMITVerified email at mit.edu

Niki Kilbertus

Technical University of Munich & Helmholtz AI

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 30, 2017	567	2017
Learning Independent Causal Mechanisms G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf International Conference on Machine Learning, ICML 2018, 2018	144	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	120	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	105*	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	69	2021
Fair decisions despite imperfect predictions N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera AISTATS 2020, 2019	48	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	47	2019
Generalization in anti-causal learning N Kilbertus, G Parascandolo, B Schölkopf NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018	42	2018
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	37	2015
A Class of Algorithms for General Instrumental Variable Models N Kilbertus, MJ Kusner, R Silva Neural Information Processing Systems (NeurIPS) 2020, 2020	25	2020
CONVWAVE: Searching for Gravitational Waves with Fully Convolutional Neural Nets T Gebhard, N Kilbertus, G Parascandolo, I Harry, B Schölkopf Workshop Deep Learning for Physical Sciences at NIPS 2017, 2017	16	2017
On component interactions in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus Neural Information Processing Systems (NeurIPS) 2021, 2021	15	2021
Quod erat knobelandum C Löh, S Krauss, N Kilbertus Springer Berlin Heidelberg, 2016	11	2016
Improving consequential decision making under imperfect predictions N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera arXiv preprint arXiv:1902.02979, 2019	8	2019
Exploration in two-stage recommender systems J Hron, K Krauth, MI Jordan, N Kilbertus arXiv preprint arXiv:2009.08956, 2020	7	2020
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	6	2022
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	6*	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	6	2022
Beyond predictions in neural odes: Identification and interventions H Aliee, FJ Theis, N Kilbertus arXiv preprint arXiv:2106.12430, 2021	6	2021
Multi-disciplinary fairness considerations in machine learning for clinical trials I Chien, N Deliu, R Turner, A Weller, S Villar, N Kilbertus 2022 ACM Conference on Fairness, Accountability, and Transparency, 906-924, 2022	4	2022

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