Matt Kusner
I am an Associate Professor in Machine Learning at University College London.
I'm interested in improving our fundamental understanding of ML (e.g., models, optimizers), and to use this understanding to create reliable, trustworthy machine learning algorithms with guarantees. This includes algorithms that preserve privacy, measure fairness, regulate model use, and provide guarantees for distribution shifts.
I am also a member of the European Laboratory for Learning and Intelligent Systems. 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, and am a guardian of the fantastic Karl & Nora.
Current graduate students (alumni):
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Preprints
Setting the Record Straight on Transformer Oversmoothing
Gbètondji Dovonon, Michael Bronstein, Matt J. Kusner paper |
Papers
Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton *=authors contributing equally, listed in alphabetical order The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024. paper |
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
Jean Kaddour*, Oscar Key*, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner *=authors contributing equally, listed in alphabetical order Neural Information Processing Systems (NeurIPS), 2023. paper |
Adapting to Latent Subgroup Shifts via Concepts and Proxies
Ibrahim Alabdulmohsin*, Nicole Chiou*, Alexander D’Amour*, Arthur Gretton*, Sanmi Koyejo*, Matt J. Kusner*, Stephen R. Pfohl*, Olawale Salaudeen*, Jessica Schrouff*, Katherine Tsai* *=authors contributing equally, listed in alphabetical order The International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. paper | poster |
DAG Learning on the Permutahedron
Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae The International Conference on Learning Representations (ICLR), 2023. paper |
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus Conference on Causal Learning and Reasoning (CLeaR), 2023. Oral Presentation. paper |
When Do Flat Minima Optimizers Work?
Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner Neural Information Processing Systems (NeurIPS), 2022. paper |
Local Latent Space Bayesian Optimization over Structured Inputs
Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner Neural Information Processing Systems (NeurIPS), 2022. paper |
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva The Conference on Uncertainty in Artificial Intelligence (UAI), 2022. Oral Presentation. paper |
MPC-Friendly Commitments for Publicly Verifiable Covert Security
Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner The Conference on Computer and Communications Security (CCS), 2021. paper |
Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva Neural Information Processing Systems (NeurIPS), 2021. paper | code |
Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt J. Kusner The International Conference on Computer Vision (ICCV), 2021. paper | code |
Learning Binary Decision Trees by Argmin Differentiation
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae The International Conference on Machine Learning (ICML), 2021. paper | code |
Operationalizing Complex Causes: A Pragmatic View of Mediation
Limor Gultchin, David Watson, Matt J. Kusner, Ricardo Silva The International Conference on Machine Learning (ICML), 2021. paper | code |
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Afsaneh Mastouri* Yuchen Zhu*, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton', Krikamol Muandet' *,'=authors contributing equally The International Conference on Machine Learning (ICML), 2021. paper | code |
Counterfactual Data Augmentation for Neural Machine Translation
Qi Liu, Matt J. Kusner, Phil Blunsom North American Chapter of the Association for Computational Linguistics (NAACL), 2021. paper |
A Class of Algorithms for General Instrumental Variable Models
Niki Kilbertus, Matt J. Kusner, Ricardo Silva Neural Information Processing Systems (NeurIPS), 2020. paper | code |
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato Neural Information Processing Systems (NeurIPS), 2020. Spotlight Presentation. paper | code |
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 | code |
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 | code |
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
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 |
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 |