Niki Kilbertus
My mission is to build socially beneficial, robust, and theoretically substantiated machine learning systems.
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. 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
- 11/2018: "Generalization in anti-causal learning" accepted at the NeurIPS18 Workshop on Critiquing and Correcting Trends in Machine Learning
- 08/2018: Please consider submitting to our NeuriPS18 Workshop on Privacy Preserving Machine Learning
- 05/2018: Two papers accepted at ICML 2018
- 04/2018: I will join Amazon for a summer internship
- 11/2017: "Learning Independent Causal Mechanisms" accepted at NIPS workshop on Learning Disentangled Representations
- 11/2017: "ConvWave" accepted at NIPS workshop on Deep Learning for Physical Sciences
- 09/2017: "Avoiding Discrimination Through Causal Reasoning" accepted at NIPS
Selected Publications & Projects
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] [in the press: Financial Times, New Scientist, Second Nexus, The Alan Turing Institute, MPI]
Learning Independent Causal Mechanisms
Giambattista Parascandolo, NK, Mateo Rojas-Carulla, Bernhard Schölkopf
ICML 2018
also at: NIPS 2017 Workshop on Learning Disentangled Representations
Avoiding Discrimination Through Causal Reasoning
NK, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
NIPS 2017
[paper] [bibtex] [poster] [in the press: MPI] [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)
NIPS 2017 Workshop on Deep Learning for Physical Sciences
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)]
Master Thesis Mathematics: Numerical Analysis of Causal Fermion Systems on R x S^3
NK
[thesis (pdf, ~3.8MB)]
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
- Organizer of the NeurIPS 2018 workshop on Privacy Preserving Machine Learning
- Organizer of the CamTue workshop: Mallorca 2017, Tenerife 2018
- I thoroughly enjoy teaching, was active in the Schülerzirkel Mathematik in Regensburg, a TA for many courses in Math, Physics, and CS, lectured a semi-annual course on Computer- and Microcontroller-Technology, and co-lectured the course Green-IT at the summer academy 2016 in Leysin, organized by the German Academic Scholarship Foundation.
- I like building things, for example: Babyzen - A flexible sensor BoosterPack [codeproject article][short video][report (pdf)] or some things here.
Contact
[last] [dot] [first] [at] gmail [dot] com
