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ORCIDMatt J. Kusner
Matthew J. Kusner
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- affiliation: University College London, Department of Computer Science, UK
- affiliation: Alan Turing Institute, London, UK
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2020 – today
- 2024
[c41]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. AISTATS 2024: 3961-3969- [i39]Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner:
Setting the Record Straight on Transformer Oversmoothing. CoRR abs/2401.04301 (2024) - [i38]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. CoRR abs/2403.07442 (2024) - [i37]Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner:
An Auditing Test To Detect Behavioral Shift in Language Models. CoRR abs/2410.19406 (2024) - 2023
- [c40]Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS 2023: 9637-9661 - [c39]Kirtan Padh
, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR 2023: 142-176 - [c38]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. ICLR 2023 - [c37]Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner:
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. NeurIPS 2023 - [i36]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. CoRR abs/2301.11898 (2023) - [i35]Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner:
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. CoRR abs/2307.06440 (2023) - 2022
- [c36]Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff:
Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022. AFCP 2022: 1-6 - [c35]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
When Do Flat Minima Optimizers Work? NeurIPS 2022 - [c34]Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner:
Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS 2022 - [c33]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI 2022: 2414-2424 - [e1]Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff:
Algorithmic Fairness through the Lens of Causality and Privacy Workshop, AFCP 2022, New Orleans, LA, USA (hybrid), 03 December 2022. Proceedings of Machine Learning Research 214, PMLR 2022 [contents] - [i34]Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner:
Local Latent Space Bayesian Optimization over Structured Inputs. CoRR abs/2201.11872 (2022) - [i33]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR abs/2202.00661 (2022) - [i32]Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CoRR abs/2202.10806 (2022) - [i31]Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu:
Evaluating Self-Supervised Learning for Molecular Graph Embeddings. CoRR abs/2206.08005 (2022) - [i30]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR abs/2206.09186 (2022) - [i29]Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Machine Learning: A Survey and Open Problems. CoRR abs/2206.15475 (2022) - [i28]Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR abs/2212.11254 (2022) - [i27]Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner:
MPC-Friendly Commitments for Publicly Verifiable Covert Security. IACR Cryptol. ePrint Arch. 2022: 102 (2022) - 2021
- [c32]Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner:
MPC-Friendly Commitments for Publicly Verifiable Covert Security. CCS 2021: 2685-2704 - [c31]Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt J. Kusner:
Unsupervised Point Cloud Pre-training via Occlusion Completion. ICCV 2021: 9762-9772 - [c30]Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva:
Operationalizing Complex Causes: A Pragmatic View of Mediation. ICML 2021: 3875-3885 - [c29]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML 2021: 7512-7523 - [c28]Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae:
Learning Binary Decision Trees by Argmin Differentiation. ICML 2021: 12298-12309 - [c27]Qi Liu, Matt J. Kusner, Phil Blunsom:
Counterfactual Data Augmentation for Neural Machine Translation. NAACL-HLT 2021: 187-197 - [c26]Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Effect Inference for Structured Treatments. NeurIPS 2021: 24841-24854 - [i26]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR abs/2105.04544 (2021) - [i25]Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva:
Graph Intervention Networks for Causal Effect Estimation. CoRR abs/2106.01939 (2021) - [i24]Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva:
Operationalizing Complex Causes: A Pragmatic View of Mediation. CoRR abs/2106.05074 (2021) - [i23]Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner:
MPC-Friendly Commitments for Publicly Verifiable Covert Security. CoRR abs/2109.07461 (2021) - 2020
- [c25]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. AISTATS 2020: 3970-3979 - [c24]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS 2020 - [c23]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. NeurIPS 2020 - [i22]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. CoRR abs/2003.01461 (2020) - [i21]Qi Liu, Matt J. Kusner, Phil Blunsom:
A Survey on Contextual Embeddings. CoRR abs/2003.07278 (2020) - [i20]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. CoRR abs/2006.06366 (2020) - [i19]Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner:
Pre-Training by Completing Point Clouds. CoRR abs/2010.01089 (2020) - [i18]Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae:
Learning Binary Trees via Sparse Relaxation. CoRR abs/2010.04627 (2020) - [i17]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR abs/2012.11522 (2020)
2010 – 2019
- 2019
- [c22]Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón:
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CCS 2019: 1231-1247 - [c21]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Generative Model For Electron Paths. ICLR (Poster) 2019 - [c20]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Generating Molecules via Chemical Reactions. DGS@ICLR 2019 - [c19]Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Making Decisions that Reduce Discriminatory Impacts. ICML 2019: 3591-3600 - [c18]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. NeurIPS 2019: 7935-7947 - [c17]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI 2019: 616-626 - [i16]Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng:
Gradient Regularized Budgeted Boosting. CoRR abs/1901.04065 (2019) - [i15]John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato:
A Model to Search for Synthesizable Molecules. CoRR abs/1906.05221 (2019) - [i14]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR abs/1907.01040 (2019) - [i13]Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón:
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CoRR abs/1907.03372 (2019) - [i12]Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris:
Cumulo: A Dataset for Learning Cloud Classes. CoRR abs/1911.04227 (2019) - 2018
- [c16]David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato:
Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) 2018 - [c15]Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller:
Blind Justice: Fairness with Encrypted Sensitive Attributes. ICML 2018: 2635-2644 - [c14]Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade:
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. ICML 2018: 4497-4506 - [i11]Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva:
Causal Reasoning for Algorithmic Fairness. CoRR abs/1805.05859 (2018) - [i10]John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Predicting Electron Paths. CoRR abs/1805.10970 (2018) - [i9]Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Causal Interventions for Fairness. CoRR abs/1806.02380 (2018) - [i8]Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller:
Blind Justice: Fairness with Encrypted Sensitive Attributes. CoRR abs/1806.03281 (2018) - [i7]Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade:
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR abs/1806.03461 (2018) - 2017
- [c13]Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato:
Grammar Variational Autoencoder. ICML 2017: 1945-1954 - [c12]Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva:
Counterfactual Fairness. NIPS 2017: 4066-4076 - [c11]Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva:
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. NIPS 2017: 6414-6423 - [i6]Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva:
Counterfactual Fairness. CoRR abs/1703.06856 (2017) - [i5]David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato:
Learning a Generative Model for Validity in Complex Discrete Structures. CoRR abs/1712.01664 (2017) - 2016
- [c10]Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger:
Private Causal Inference. AISTATS 2016: 1308-1317 - [c9]Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger:
Supervised Word Mover's Distance. NIPS 2016: 4862-4870 - [i4]Matt J. Kusner, José Miguel Hernández-Lobato:
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. CoRR abs/1611.04051 (2016) - 2015
- [c8]Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger:
Differentially Private Bayesian Optimization. ICML 2015: 918-927 - [c7]Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger:
From Word Embeddings To Document Distances. ICML 2015: 957-966 - [c6]Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley:
Fast Distributed k-Center Clustering with Outliers on Massive Data. NIPS 2015: 1063-1071 - [i3]Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft:
Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR abs/1511.06421 (2015) - 2014
- [j1]Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle:
Classifier cascades and trees for minimizing feature evaluation cost. J. Mach. Learn. Res. 15(1): 2113-2144 (2014) - [c5]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 2014: 1939-1945 - [c4]Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal:
Stochastic Neighbor Compression. ICML 2014: 622-630 - [c3]Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham:
Bayesian Optimization with Inequality Constraints. ICML 2014: 937-945 - [i2]Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger:
Stochastic Covariance Compression. CoRR abs/1412.1740 (2014) - 2013
- [c2]Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen:
Cost-Sensitive Tree of Classifiers. ICML (1) 2013: 133-141 - [c1]Zhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger:
Anytime Representation Learning. ICML (3) 2013: 1076-1084 - 2012
- [i1]Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen:
Cost-Sensitive Tree of Classifiers. CoRR abs/1210.2771 (2012)
Coauthor Index
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