Niki Kilbertus

My mission is to build socially beneficial, robust, and theoretically substantiated machine learning systems.

I am a PhD student in the Cambridge-Tübingen program (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.

During my PhD I spent time at Deepmind and Amazon.

My background is in Physics and Mathematics. I was fortunate to spend time at Harvard, working with Paul Chesler and Wilke van der Schee, as well as at Stanford, working with William East and Tom Abel.

News

  • 12/2019: Honored to be one of the 10 "AI-newcomers" in Germany
  • 07/2019: Organizing a NeurIPS workshop on Human Centric Machine Learning
  • 06/2019: Improving consequential decisions under imperfect predictions @ KDD 2019 Workshop (DCCL)
  • 06/2019: Convolutional neural networks: a magic bullet for gravitational-wave detection? @ Physical Review D
  • 05/2019: The sensitivity of counterfactual fairness to unmeasured confounding @ UAI 2019
  • 02/2019: 2nd edition of our book Quod erat knobelandum is now available at Springer [German]

Selected Publications & Projects

The sensitivity of counterfactual fairness to unmeasured confounding

NK, Philip Ball, Matt Kusner, Adrian Weller, Ricardo Silva

UAI 2019

[paper] [bibtex] [code]

Convolutional neural networks: a magic bullet for gravitational-wave detection?

Timothy Gebhard*, NK*, Ian Harry, Bernhard Schölkopf (* equal contribution)

Physical Review D, 2019

[paper] [bibtex] [code] [data generation] [DOI]

Fair Decisions Despite Imperfect Predictions

NK, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera

[paper] [code]

shorter version: Improving consequential decision making under imperfect predictions

KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning (DCCL)

Generalization in anti-causal learning

NK*, Giambattista Parascandolo*, Bernhard Schölkopf (* equal contribution)

NeurIPS 2018 Workshop on Critiquing and correcting trends in machine learning

[paper]

Blind Justice: Fairness with Encrypted Sensitive Attributes

NK, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller

ICML 2018

also at: FATML 2018 [talk] and PIMLAI 2018

[paper] [bibtex] [poster] [code]

Learning Independent Causal Mechanisms

Giambattista Parascandolo, NK, Mateo Rojas-Carulla, Bernhard Schölkopf

ICML 2018

also at: NIPS 2017 Workshop on Learning Disentangled Representations

[paper] [bibtex]

Avoiding Discrimination Through Causal Reasoning

NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

NeurIPS 2017

[paper] [bibtex] [poster]

ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

Timothy Gebhard*, NK*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf (* equal contribution)

NeurIPS 2017 Workshop on Deep Learning for Physical Sciences

[paper] [bibtex] [code] [poster]

Universal Hydrodynamic Flow in Holographic Planar Shock Collisions

Paul Chesler, NK, Wilke van der Schee

Journal for High Energy Physics, 2015

[paper, arxiv version] [detailed project report (pdf, ~1MB)]

Quod erat knobelandum

Clara Löh, Stefan Krauss, NK

Springer Spektrum, (1st edition: 2016, 2nd edition: 2019)

[springer] [amazon]

Master Thesis Physics: Numerical Analysis of Gravitational Wave Generation during Metric Preheating

NK

[thesis (pdf, ~12MB)] [code]

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

NK

[thesis (pdf, ~3.8MB)]

Physics Project: Sky-MoCa: The Skyrmion Phase in 3D Lattice Simulations

NK

[report] [code]

News mentions and science communication


Talks

  • Albert Einstein Institute (Potsdam-Golm, Germany): Machine Learning powered CBC Search
  • Alan Turing Institute (London, UK): Fairness in Machine Learning
  • Max Planck Institute for Software Systems (Saarbrücken, Germany): Fairness in Machine Learning
  • Stanford University (CA, USA): Searching for Gravitational Waves with Machine Learning
  • University of Regensburg (Regensburg, Germany): Fully Convolutional Networks for Gravitational Wave Searches
  • Microsoft Research (Cambridge, UK): Learning Independent Causal Mechanisms
  • Amazon Research (Cambridge, UK): Blind Justice: Fairness with Encrypted Sensitive Attributes

Service to the community

Miscellaneous

Contact

[last] [dot] [first] [at] gmail [dot] com