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

Selected Publications & Projects

The sensitivity of counterfactual fairness to unmeasured confounding

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

UAI 2019

[paper] [code]

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

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

Physical Review D (accepted, coming soon), 2019

[paper] [code] [data generation]

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] [related grant: Digital Impact Grant by Stanford PACS]

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

Miscellaneous

Contact

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