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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
4562017
Learning Independent Causal Mechanisms
G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf
International Conference on Machine Learning, ICML 2018, 2018
1012018
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
862018
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
75*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
332019
Generalization in anti-causal learning
N Kilbertus, G Parascandolo, B Schölkopf
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
332018
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
332015
Fair decisions despite imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
AISTATS 2020, 2019
292019
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
262021
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
142017
A Class of Algorithms for General Instrumental Variable Models
N Kilbertus, MJ Kusner, R Silva
Neural Information Processing Systems (NeurIPS) 2020, 2020
112020
Quod erat knobelandum
C Löh, S Krauss, N Kilbertus
Springer Berlin Heidelberg, 2016
112016
Improving consequential decision making under imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
82019
Exploration in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
Workshop on Bandit and Reinforcement Learning from User Interactions at …, 2020
42020
On component interactions in two-stage recommender systems
J Hron, K Krauth, MI Jordan, N Kilbertus
Neural Information Processing Systems (NeurIPS) 2021, 2021
32021
Baby Zen-a flexible sensor booster pack
F Rappl, N Kilbertus, F Wunsch
Texas instruments Innovation Challenge: Europe Design contest, 2015
22015
A causal view on compositional data
E Ailer, CL Müller, N Kilbertus
arXiv preprint arXiv:2106.11234, 2021
12021
Beyond traditional assumptions in fair machine learning
N Kilbertus
arXiv preprint arXiv:2101.12476, 2021
12021
Multi-disciplinary fairness considerations in machine learning for clinical trials
I Chien, N Deliu, RE Turner, A Weller, SS Villar, N Kilbertus
arXiv preprint arXiv:2205.08875, 2022
2022
Supervised Learning and Model Analysis with Compositional Data
S Huang, E Ailer, N Kilbertus, N Pfister
arXiv preprint arXiv:2205.07271, 2022
2022
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Articles 1–20