I started my PhD in the Cambridge-Tübingen program in 2016 (prospective graduation in 2020), where I am co-supervised by Bernhard Schölkopf and Carl Rasmussen. My advisor is Adrian Weller.
I am a member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft.
My background is in Physics and Mathematics. Currently my main research focus are socially beneficial, robust, and theoretically substantiated machine learning systems.
Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.
Fair Decisions Despite Imperfect Predictions
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)
Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.
Convolutional neural networks: A magic bullet for gravitational-wave detection?
Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)
Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)
Kilbertus*, N., Parascandolo*, G., Schölkopf*, B.
Generalization in anti-causal learning
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, December 2018, *authors are listed in alphabetical order (conference)
Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., Weller, A.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 2635-2644, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., Schölkopf, B.
Learning Independent Causal Mechanisms
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4033-4041, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
Parascandolo, G., Rojas-Carulla, M., Kilbertus, N., Schölkopf, B.
Learning Independent Causal Mechanisms
Workshop: Learning Disentangled Representations: from Perception to Control at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)
Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B.
Avoiding Discrimination through Causal Reasoning
Advances in Neural Information Processing Systems 30, pages: 656-666, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)
Gebhard, T., Kilbertus, N., Parascandolo, G., Harry, I., Schölkopf, B.
ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets
Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)
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