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Books and Theses
- 2020
[b1]Niki Kilbertus:
Beyond traditional assumptions in fair machine learning. University of Cambridge, UK, 2020
Journal Articles
- 2024
- [j2]Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus:
Learning Counterfactually Invariant Predictors. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j1]Shimeng Huang
, Elisabeth Ailer, Niki Kilbertus, Niklas Pfister:
Supervised learning and model analysis with compositional data. PLoS Comput. Biol. 19(6) (2023)
Conference and Workshop Papers
- 2024
- [c18]Luca Eyring, Dominik Klein, Théo Uscidda, Giovanni Palla, Niki Kilbertus, Zeynep Akata, Fabian J. Theis:
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation. ICLR 2024 - [c17]Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. ICLR 2024 - 2023
- [c16]Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR 2023: 142-176 - [c15]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling content creator incentives on algorithm-curated platforms. ICLR 2023 - [c14]Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus:
Sequential Underspecified Instrument Selection for Cause-Effect Estimation. ICML 2023: 408-420 - [c13]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. ICML 2023: 1978-2002 - [c12]Alistair White, Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers:
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints. NeurIPS 2023 - 2022
- [c11]Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. FAccT 2022: 906-924 - [c10]Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian J. Theis, Niki Kilbertus:
Sparsity in Continuous-Depth Neural Networks. NeurIPS 2022 - [c9]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. NeurIPS 2022 - 2021
- [c8]Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer:
On Disentangled Representations Learned from Correlated Data. ICML 2021: 10401-10412 - [c7]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On Component Interactions in Two-Stage Recommender Systems. NeurIPS 2021: 2744-2757 - 2020
- [c6]Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Fair Decisions Despite Imperfect Predictions. AISTATS 2020: 277-287 - [c5]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. NeurIPS 2020 - 2019
- [c4]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI 2019: 616-626 - 2018
- [c3]Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller:
Blind Justice: Fairness with Encrypted Sensitive Attributes. ICML 2018: 2635-2644 - [c2]Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. ICML 2018: 4033-4041 - 2017
- [c1]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. NIPS 2017: 656-666
Informal and Other Publications
- 2024
- [i32]Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen, Cristopher Salvi, Niki Kilbertus:
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes. CoRR abs/2402.18477 (2024) - [i31]Zhufeng Li, Sandeep Suresh Cranganore, Nicholas D. Youngblut, Niki Kilbertus:
Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity. CoRR abs/2405.05998 (2024) - [i30]Elisabeth Ailer, Niclas Dern, Jason S. Hartford, Niki Kilbertus:
Targeted Sequential Indirect Experiment Design. CoRR abs/2405.19985 (2024) - [i29]Birgit Kühbacher, Fernando Iglesias-Suarez, Niki Kilbertus, Veronika Eyring:
Towards Physically Consistent Deep Learning For Climate Model Parameterizations. CoRR abs/2406.03920 (2024) - 2023
- [i28]Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus:
Sequential Underspecified Instrument Selection for Cause-Effect Estimation. CoRR abs/2302.05684 (2023) - [i27]Alistair White, Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers:
Stabilized Neural Differential Equations for Learning Constrained Dynamics. CoRR abs/2306.09739 (2023) - [i26]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. CoRR abs/2307.12617 (2023) - [i25]Stéphane d'Ascoli, Sören Becker, Alexander Mathis, Philippe Schwaller, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. CoRR abs/2310.05573 (2023) - [i24]Luca Eyring, Dominik Klein, Théo Uscidda, Giovanni Palla, Niki Kilbertus, Zeynep Akata, Fabian J. Theis:
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation. CoRR abs/2311.15100 (2023) - 2022
- [i23]Felix Finster, Robert H. Jonsson, Niki Kilbertus:
Numerical Analysis of the Causal Action Principle in Low Dimensions. CoRR abs/2201.06382 (2022) - [i22]Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CoRR abs/2202.10806 (2022) - [i21]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting single-cell perturbation responses for unseen drugs. CoRR abs/2204.13545 (2022) - [i20]Shimeng Huang, Elisabeth Ailer, Niki Kilbertus, Niklas Pfister:
Supervised Learning and Model Analysis with Compositional Data. CoRR abs/2205.07271 (2022) - [i19]Isabel Chien, Nina Deliu, Richard E. Turner, Adrian Weller, Sofia S. Villar, Niki Kilbertus:
Multi-disciplinary fairness considerations in machine learning for clinical trials. CoRR abs/2205.08875 (2022) - [i18]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean:
Modeling Content Creator Incentives on Algorithm-Curated Platforms. CoRR abs/2206.13102 (2022) - [i17]Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Niki Kilbertus, Yucen Luo:
Learning Counterfactually Invariant Predictors. CoRR abs/2207.09768 (2022) - [i16]Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian J. Theis, Niki Kilbertus:
Sparsity in Continuous-Depth Neural Networks. CoRR abs/2210.14672 (2022) - [i15]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Discovering ordinary differential equations that govern time-series. CoRR abs/2211.02830 (2022) - 2021
- [i14]Niki Kilbertus:
Beyond traditional assumptions in fair machine learning. CoRR abs/2101.12476 (2021) - [i13]Elisabeth Ailer, Christian L. Müller, Niki Kilbertus:
A causal view on compositional data. CoRR abs/2106.11234 (2021) - [i12]Hananeh Aliee, Fabian J. Theis, Niki Kilbertus:
Beyond Predictions in Neural ODEs: Identification and Interventions. CoRR abs/2106.12430 (2021) - [i11]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
On component interactions in two-stage recommender systems. CoRR abs/2106.14979 (2021) - 2020
- [i10]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. CoRR abs/2006.06366 (2020) - [i9]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i8]Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus:
Exploration in two-stage recommender systems. CoRR abs/2009.08956 (2020) - 2019
- [i7]Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Improving Consequential Decision Making under Imperfect Predictions. CoRR abs/1902.02979 (2019) - [i6]Timothy D. Gebhard, Niki Kilbertus, Ian Harry, Bernhard Schölkopf:
Convolutional neural networks: a magic bullet for gravitational-wave detection? CoRR abs/1904.08693 (2019) - [i5]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR abs/1907.01040 (2019) - 2018
- [i4]Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller:
Blind Justice: Fairness with Encrypted Sensitive Attributes. CoRR abs/1806.03281 (2018) - [i3]Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf:
Generalization in anti-causal learning. CoRR abs/1812.00524 (2018) - 2017
- [i2]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. CoRR abs/1706.02744 (2017) - [i1]Giambattista Parascandolo, Mateo Rojas-Carulla, Niki Kilbertus, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. CoRR abs/1712.00961 (2017)
Coauthor Index
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