Niki Kilbertus
Niki Kilbertus
Helmholtz AI
Verified email at helmholtz-muenchen.de - Homepage
Title
Cited by
Cited by
Year
Avoiding discrimination through causal reasoning
N Kilbertus, M Rojas-Carulla, G Parascandolo, M Hardt, D Janzing, ...
arXiv preprint arXiv:1706.02744, 2017
3122017
Learning Independent Causal Mechanisms
G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf
International Conference on Machine Learning, ICML 2018, 2018
532018
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
512018
Convolutional neural networks: A magic bullet for gravitational-wave detection?
TD Gebhard, N Kilbertus, I Harry, B Schölkopf
Physical Review D 100 (6), 063015, 2019
432019
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
312015
Fair decisions despite imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
AISTATS 2020, 2019
18*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
172019
Generalization in anti-causal learning
N Kilbertus, G Parascandolo, B Schölkopf
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
172018
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
122017
Quod erat knobelandum
C Löh, S Krauss, N Kilbertus
Springer Spektrum, 2016
8*2016
Is independence all you need? on the generalization of representations learned from correlated data
F Träuble, E Creager, N Kilbertus, A Goyal, F Locatello, B Schölkopf, ...
arXiv preprint arXiv:2006.07886, 2020
72020
Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA
T Gebhard, N Kilbertus, G Parascandolo, I Harry, B Schölkopf
52017
A Class of Algorithms for General Instrumental Variable Models
N Kilbertus, MJ Kusner, R Silva
Neural Information Processing Systems (NeurIPS) 2020, 2020
32020
Bernhard Sch olkopf. 2017. Avoiding Discrimination through Causal Reasoning
N Kilbertus, MR Carulla, G Parascandolo, M Hardt, D Janzing
Proc. NIPS, 0
3
Baby Zen-a flexible sensor booster pack
F Rappl, N Kilbertus, F Wunsch
Texas instruments Innovation Challenge: Europe Design contest, 2015
22015
Exploration in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
arXiv preprint arXiv:2009.08956, 2020
12020
Beyond traditional assumptions in fair machine learning
N Kilbertus
arXiv preprint arXiv:2101.12476, 2021
2021
Analyzing Non-self-dual Solutions in the CP-N-1 Model
N Kilbertus
2014
Der Dirac-Operator auf Riemannschen Mannigfaltigkeiten
N Kilbertus
2013
CW: Searching for Gravitational Waves with Fully Convolutional Neural Nets
T Gebhard, N Kilbertus, G Parascandolo, I Harry, B Schölkopf
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Articles 1–20