Niki Kilbertus

I'm a professor at the Technical University of Munich in the Informatics Department and a group leader at Helmholtz AI working on causality and socially beneficial machine learning.

I obtained my PhD in the Cambridge-Tübingen program co-supervised by Bernhard Schölkopf, Carl Rasmussen, and Adrian Weller. I was an Ellis student and member of Pembroke College, funded by the Cambridge-Tübingen PhD fellowship with generous donations from Microsoft.

During my PhD I interned at Deepmind, Google, and Amazon.

I grew up in Austria, studied Physics and Maths in Regensburg, and was fortunate to spend time at Harvard and Stanford during my studies.

News

• 07/2022: Sören, Cecilia, and I will be co-organizing a NeurIPS workshop on A causal view on dynamical systems

• 05/2022: I am looking for a postdoc: application info

• 04/2022: Multi-disciplinary fairness considerations in machine learning for clinical trials @FAccT

• 12/2021: I am now Assistant Professor at the Technical University of Munich

• 09/2021: On component interactions in two-stage recommender systems @NeurIPS2021

• 08/2021: Growing and growing: newest PhD student Sören Becker joined our group 🎉

• 07/2021: Co-organizing the NeurIPS workshop Machine Learning Meets Econometrics

• 04/2021: Co-organizing an ICML workshop on the Neglected Assumptions in Causal Inference

Group members

PhD candidates

• Elisabeth Ailer

• Sören Becker

• Zhufeng Li

• Kirtan Padh

• Till Richter (jointly with Fabian Theis and Yoshua Bengio)

• Cecilia Casolo

MSc students

• Luca Eyring

BSc students

• Amin Ben Saad

• Ronald Skorobogat

Alumni

• Michal Klein (MSc student)

• Simon Böhm (MSc student)

• Alexander Reisach (MSc student)

Prospective students

MSc, BSc, HiWi, guided research, internships

If you are interested in doing your MSc or BSc thesis with me or a guided research project (only for TUM students) please fill out this form and indicate that you are interested in working with me directly. (This form is maintained by people at the Helmholtz Center, but I also use it for TUM students and supervision of external students.)

PhD students

I advertise all openings for PhD students on this homepage as well as on twitter. If there are no current openings, please feel free to reach out directly via email and let me know why you'd be a great fit for the group.

Postdocs

If you're interested in doing a postdoc with me, please reach out directly via email.

Selected publications

• Sparsity in Continuous-Depth Neural Networks
  Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian Theis, NK
  NeurIPS 2022

• Learning Counterfactually Invariant Predictors [paper]
  Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, NK, Yucen Luo

• Modeling Content Creator Incentives on Algorithm-Curated Platforms [paper]
  Jiri Hron, Karl Krauth, Michael I. Jordan, NK, Sarah Dean

• Supervised Learning and Model Analysis with Compositional Data [paper] [code]
  Shimeng Huang, Elisabeth Ailer, NK, Niklas Pfister

• Multi-disciplinary fairness considerations in machine learning for clinical trials [paper]
  Isabel Chien, Nina Deliu, Richard Turner, Adrian Weller, Sofia Villar, NK
  FAccT 2022

• Predicting single-cell perturbation responses for unseen drugs [paper] [code]
  Leon Hetzel*, Simon Böhm*, NK, Stephan Günnemann, Mohammad Lotfollahi, Fabian Theis (* equal contribution)
  NeurIPS 2022

• Stochastic Causal Programming for Bounding Treatment Effects [paper]
  Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, NK

• Numerical Analysis of the Causal Action Principle in Low Dimensions [paper]
  Felix Finster, Robert H. Jonsson, NK

• On component interactions in two-stage recommender systems [paper]
  Jiri Hron, Karl Krauth, Michael I. Jordan, NK
  NeurIPS 2021

• Beyond Predictions in Neural ODEs: Identification and Interventions [paper]
  Hananeh Aliee, Fabian Theis, NK

• A causal view on compositional data [paper]
  Elisabeth Ailer, Christian L. Müller, NK

• On Disentangled Representations Learned From Correlated Data [paper]
  Frederik Träuble, Elliot Creager, NK, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer
  ICML 2021

• Exploration in two-stage recommender systems [paper] [short talk video]
  Jiri Hron*, Karl Krauth*, Michael I. Jordan, NK (* equal contribution)
  ACM RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL 2020)
  NeurIPS 2020 Workshop on Consequential Decisions in Dynamic Environments
  NeurIPS 2020 Workshop on Challenges of Real-World RL

• A class of algorithms for general instrumental variable models [paper] [code] [talk video]
  NK, Matt J. Kusner, Ricardo Silva
  NeurIPS 2020

• Fair decisions despite imperfect predictions [paper] [bibtex] [code]
  NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
  AISTATS 2020

• The sensitivity of counterfactual fairness to unmeasured confounding [paper] [bibtex] [code]
  NK, Philip Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
  UAI 2019

• Convolutional neural networks: a magic bullet for gravitational-wave detection? [paper] [bibtex] [code] [data generation] [DOI]
  Timothy Gebhard*, NK*, Ian Harry, Bernhard Schölkopf (* equal contribution)
  Physical Review D 2019

• Improving consequential decision making under imperfect predictions [paper]
  NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera
  KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning (DCCL)

• Generalization in anti-causal learning [paper]
  NK*, Giambattista Parascandolo*, Bernhard Schölkopf (* equal contribution)
  NeurIPS 2018 Workshop on Critiquing and correcting trends in machine learning

• Blind Justice: Fairness with Encrypted Sensitive Attributes [paper] [bibtex] [poster] [code]
  NK, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
  ICML 2018

• Learning Independent Causal Mechanisms [paper] [bibtex]
  Giambattista Parascandolo, NK, Mateo Rojas-Carulla, Bernhard Schölkopf
  ICML 2018

• Avoiding Discrimination Through Causal Reasoning [paper] [bibtex] [poster]
  NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
  NeurIPS 2017

• ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets [paper] [bibtex] [code] [poster]
  Timothy Gebhard*, NK*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf (* equal contribution)
  NeurIPS 2017 Workshop on Deep Learning for Physical Sciences

• Universal Hydrodynamic Flow in Holographic Planar Shock Collisions [paper, arxiv version] [detailed project report]
  Paul Chesler, NK, Wilke van der Schee
  Journal for High Energy Physics, 2015

PhD thesis

Beyond traditional assumptions in fair machine learning [thesis pdf (arxiv)]
PhD Thesis @ University of Cambridge

Book

Quod erat knobelandum [springer] [amazon]
Clara Löh, Stefan Krauss, NK
Springer Spektrum (1st edition: 2016, 2nd edition: 2019)

Theses and projects

• Master Thesis Physics: Numerical Analysis of Gravitational Wave Generation during Metric Preheating [thesis] [code]

• Master Thesis Mathematics: Numerical Analysis of Causal Fermion Systems on R x S^3 [thesis]

• Physics Research Project: Sky-MoCa: The Skyrmion Phase in 3D Lattice Simulations [report] [code]

Activities

• Co-organizing a workshop on A causal view on dynamical systems @ NeurIPS2022

• Co-organized a workshop on Machine Learning meets Econometrics @ NeurIPS2021

• Co-organized a workshop on the Neglected Assumptions in Causal Inference @ ICML2021

• Co-organized a workshop on Consequential Decisions in Dynamic Environments @ NeurIPS2020

• Co-organized the Human Centric Machine Learning workshop @ NeurIPS2019

• Co-organized the Privacy Preserving Machine Learning workshop @ NeurIPS2018

• Organized the CamTue workshop on Tenerife in 2018 and on Mallorca in 2017