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Niki Kilbertus
Niki Kilbertus
Technical University of Munich & Helmholtz AI
Verified email at tum.de - Homepage
Title
Cited by
Cited by
Year
Avoiding discrimination through causal reasoning
N Kilbertus, M Rojas Carulla, G Parascandolo, M Hardt, D Janzing, ...
Advances in neural information processing systems 30, 2017
5142017
Learning Independent Causal Mechanisms
G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf
International Conference on Machine Learning, ICML 2018, 2018
1272018
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
1102018
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
91*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
532021
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
412019
Generalization in anti-causal learning
N Kilbertus, G Parascandolo, B Schölkopf
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
382018
Fair decisions despite imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
AISTATS 2020, 2019
372019
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
362015
A Class of Algorithms for General Instrumental Variable Models
N Kilbertus, MJ Kusner, R Silva
Neural Information Processing Systems (NeurIPS) 2020, 2020
202020
Improving consequential decision making under imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
15*2019
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
152017
Quod erat knobelandum
C Löh, S Krauss, N Kilbertus
Springer Berlin Heidelberg, 2016
13*2016
On component interactions in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
Neural Information Processing Systems (NeurIPS) 2021, 2021
102021
Beyond Predictions in Neural ODEs: Identification and Interventions
H Aliee, FJ Theis, N Kilbertus
arXiv preprint arXiv:2106.12430, 2021
32021
Modeling content creator incentives on algorithm-curated platforms
J Hron, K Krauth, MI Jordan, N Kilbertus, S Dean
arXiv preprint arXiv:2206.13102, 2022
22022
Predicting single-cell perturbation responses for unseen drugs
L Hetzel, S Böhm, N Kilbertus, S Günnemann, M Lotfollahi, F Theis
arXiv preprint arXiv:2204.13545, 2022
22022
Stochastic Causal Programming for Bounding Treatment Effects
K Padh, J Zeitler, D Watson, M Kusner, R Silva, N Kilbertus
arXiv preprint arXiv:2202.10806, 2022
22022
A causal view on compositional data
E Ailer, CL Müller, N Kilbertus
arXiv preprint arXiv:2106.11234, 2021
22021
Beyond traditional assumptions in fair machine learning
N Kilbertus
arXiv preprint arXiv:2101.12476, 2021
22021
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