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Niki Kilbertus
Niki Kilbertus
Technical University of Munich & Helmholtz AI
Bestätigte E-Mail-Adresse bei tum.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Avoiding discrimination through causal reasoning
N Kilbertus, M Rojas Carulla, G Parascandolo, M Hardt, D Janzing, ...
Advances in neural information processing systems 30, 2017
5292017
Learning Independent Causal Mechanisms
G Parascandolo, N Kilbertus, M Rojas-Carulla, B Schölkopf
International Conference on Machine Learning, ICML 2018, 2018
1302018
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
1152018
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
97*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
552021
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
442019
Fair decisions despite imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
AISTATS 2020, 2019
422019
Generalization in anti-causal learning
N Kilbertus, G Parascandolo, B Schölkopf
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
372018
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
372015
A Class of Algorithms for General Instrumental Variable Models
N Kilbertus, MJ Kusner, R Silva
Neural Information Processing Systems (NeurIPS) 2020, 2020
212020
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
Improving consequential decision making under imperfect predictions
N Kilbertus, M Gomez-Rodriguez, B Schölkopf, K Muandet, I Valera
14*2019
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
112021
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
52022
Beyond predictions in neural odes: Identification and interventions
H Aliee, FJ Theis, N Kilbertus
arXiv preprint arXiv:2106.12430, 2021
52021
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
32022
Learning counterfactually invariant predictors
F Quinzan, C Casolo, K Muandet, N Kilbertus, Y Luo
arXiv preprint arXiv:2207.09768, 2022
22022
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
22022
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
22022
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