Bio
Curriculum Vitae matt.kusner@gmail.com

Matt Kusner

I am an associate professor in machine learning at University College London.

I'm interested in simple algorithms that solve specific problems. Some of my recent work includes:
Previously I was an associate professor at the University of Oxford. I was in the first group of research fellows at The Alan Turing Institute, and I did my Ph.D. with Kilian Weinberger. I am married to the wonderful Sonia Rego.

Current graduate students:

Preprints

A Survey on Contextual Embeddings
Qi Liu, Matt J. Kusner, Phil Blunsom
paper
Cumulo: A Dataset for Learning Cloud Classes
Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris
NeurIPS Workshop Tackling Climate Change with Machine Learning, 2019. Best Paper Award.
paper
Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva
paper

Papers

The long road to fairer algorithms
Matt J. Kusner, Joshua R. Loftus
Nature (Comment), 2020.
paper
Differentiable Causal Backdoor Discovery
Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
paper
A Model to Search for Synthesizable Molecules
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
Neural Information Processing Systems (NeurIPS), 2019.
paper
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
The Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
paper | code
Making Decisions that Reduce Discriminatory Impact
Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
The International Conference on Machine Learning (ICML), 2019.
paper | code | slides
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Nitin Agrawal*, Ali Shahin Shamsabadi*, Matt J. Kusner, Adrià Gascón
*=authors contributing equally
The Conference on Computer and Communications Security (CCS), 2019.
paper
A Generative Model For Electron Paths
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
The International Conference on Learning Representations (ICLR), 2019.
paper | code
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
The International Conference on Machine Learning (ICML), 2018.
paper | code
Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna Gummadi, Adrian Weller
The International Conference on Machine Learning (ICML), 2018.
paper | code
Learning a Generative Model for Validity in Complex Discrete Structures
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
The International Conference on Learning Representations (ICLR), 2018.
paper
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Chris Russell*, Matt J. Kusner*, Joshua R. Loftus, Ricardo Silva
Neural Information Processing Systems (NeurIPS), 2017.
paper
Counterfactual Fairness
Matt J. Kusner*, Joshua R. Loftus*, Chris Russell*, Ricardo Silva
Neural Information Processing Systems (NeurIPS), 2017. Oral Presentation.
paper | code | talk | poster
Grammar Variational Autoencoder
Matt J. Kusner*, Brooks Paige*, José Miguel Hernández-Lobato
The International Conference on Machine Learning (ICML), 2017.
paper | code | talk
Learning in the Real World: Constraints on Cost, Space, and Privacy
Matt J. Kusner
Ph.D. Thesis, 2016.
paper
Supervised Word Mover's Distance
Gao Huang*, Chuan Guo*, Matt J. Kusner, Yu Sun, Kilian Q. Weinberger, Fei Sha
Neural Information Processing Systems (NeurIPS), 2016. Oral Presentation.
paper | code | talk
Private Causal Inference
Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. Oral Presentation.
paper | code
Fast Distributed k-Center Clustering with Outliers on Massive Data
Gustavo Malkomes Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Neural Information Processing Systems (NeurIPS), 2015.
paper
From Word Embeddings To Document Distances
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger
The International Conference on Machine Learning (ICML), 2015.
paper | code | talk | slides | poster
Differentially Private Bayesian Optimization
Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
The International Conference on Machine Learning (ICML), 2015.
paper | talk | slides | poster
Classifier Cascades and Trees for Minimizing Feature Evaluation Cost
Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle
The Journal of Machine Learning Research (JMLR), 2014.
paper
Feature-Cost Sensitive Learning with Submodular Trees of Classifiers
Matt J. Kusner, Wenlin Chen, Quan Zhou, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Yixin Chen
AAAI Conference on Artificial Intelligence (AAAI), 2014.
paper | code | poster
Stochastic Neighbor Compression
Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal
The International Conference on Machine Learning (ICML), 2014.
paper | code | slides | poster
Bayesian Optimization with Inequality Constraints
Jacob R. Gardner Matt J. Kusner, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, John P. Cunningham
The International Conference on Machine Learning (ICML), 2014.
paper
Anytime Feature Learning
Zhixiang (Eddie) Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger
The International Conference on Machine Learning (ICML), 2013.
paper
Cost-Sensitive Tree of Classifiers
Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
The International Conference on Machine Learning (ICML), 2013.
paper

Technical Reports

Deep Manifold Traversal: Changing Labels with Convolutional Features
Jacob R. Gardner*, Paul Upchurch*, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft
paper