Matt J. Kusner

I am a research fellow at the Alan Turing Institute in London. My work is in differential privacy, document distances, dataset compression, budgeted learning, submodular optimization, and Bayesian optimization.

Previously I was a member of Kilian Weinberger's machine learning research group. CV, Contact

Preprints

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


Publications

DRAFT Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Kilian Q. Weinberger, Fei Sha
Supervised Word Mover's Distance [Oral Presentation]
Neural Information Processing Systems (NIPS), 2016

PDF Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Private Causal Inference [Oral Presentation]
Artificial Intelligence and Statistics (AISTATS), 2016

PDF Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Fast Distributed k-Center Clustering with Outliers on Massive Data
Neural Information Processing Systems (NIPS), 2015

PDF SLIDES POSTER TALK Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger
From Word Embeddings To Document Distances
The International Conference on Machine Learning (ICML), 2015

PDF SLIDES POSTER TALK Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
Differentially Private Bayesian Optimization
International Conference on Machine Learning (ICML), 2015

PDF Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle
Classifier Cascades and Trees for Minimizing Feature Evaluation Cost
Journal of Machine Learning Research (JMLR), 2014

PDF POSTER Matt J. Kusner, Wenlin Chen, Quan Zhou, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Yixin Chen
Feature-Cost Sensitive Learning with Submodular Trees of Classifiers
AAAI Conference on Artificial Intelligence (AAAI), 2014

PDF SLIDES POSTER Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal
Stochastic Neighbor Compression
International Conference on Machine Learning (ICML), 2014

PDF Jacob R. Gardner, Matt J. Kusner, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, John P. Cunningham
Bayesian Optimization with Inequality Constraints
International Conference on Machine Learning (ICML), 2014

PDF Zhixiang (Eddie) Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger
Anytime Feature Learning
International Conference on Machine Learning (ICML), 2013

PDF Zhixiang (Eddie) Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
Cost-Sensitive Tree of Classifiers
International Conference on Machine Learning (ICML), 2013


Technical Reports

PDF Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger
Image Data Compression for Covariance and Histogram Descriptors

Code

Word Mover's Distance

Code for scikit-learn compatible WMD written by the prolific Vlad Niculae: CODE
Renaud Richardet at EPFL has generously created a very nice GitHub repository from the code I released: CODE
WMD has also recently been added to the GENSIM Python library: CODE

Stochastic Neighbor Compression

Here is the first release of the code: CODE See the README within for details.

Other Things

Sudeep Das at OpenTable used the Word Mover's Distance to compare restaurant reviews.

Our work is mentioned as one of the AI breakthroughs of 2015

I am married to the wonderful Sonia Rego.