Matt J. Kusner

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 67

Top Topics

Chemical Reactions

Neural Network Training

Sensitive Attributes

Top Venues

Publications

Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner
Setting the Record Straight on Transformer Oversmoothing. CoRR (2024)
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)
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 (2024)
Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
DAG Learning on the Permutahedron. ICLR (2023)
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)
Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
DAG Learning on the Permutahedron. CoRR (2023)
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)
Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR (2023)
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 (2023)
Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
Stochastic Causal Programming for Bounding Treatment Effects. CoRR (2022)
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 (2022)
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)
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 (2022)
Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu
Evaluating Self-Supervised Learning for Molecular Graph Embeddings. CoRR (2022)
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR (2022)
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI (2022)
Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
Causal Machine Learning: A Survey and Open Problems. CoRR (2022)
Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS (2022)
Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR (2022)
Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
MPC-Friendly Commitments for Publicly Verifiable Covert Security. IACR Cryptol. ePrint Arch. 2022 (2022)
Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
When Do Flat Minima Optimizers Work? NeurIPS (2022)
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)
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt J. Kusner
Unsupervised Point Cloud Pre-training via Occlusion Completion. ICCV (2021)
Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva
Causal Effect Inference for Structured Treatments. NeurIPS (2021)
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 (2021)
Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
Operationalizing Complex Causes: A Pragmatic View of Mediation. ICML (2021)
Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva
Graph Intervention Networks for Causal Effect Estimation. CoRR (2021)
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
Learning Binary Decision Trees by Argmin Differentiation. ICML (2021)
Qi Liu, Matt J. Kusner, Phil Blunsom
Counterfactual Data Augmentation for Neural Machine Translation. NAACL-HLT (2021)
Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
MPC-Friendly Commitments for Publicly Verifiable Covert Security. CoRR (2021)
Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
MPC-Friendly Commitments for Publicly Verifiable Covert Security. CCS (2021)
Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
Operationalizing Complex Causes: A Pragmatic View of Mediation. CoRR (2021)
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 (2020)
Qi Liu, Matt J. Kusner, Phil Blunsom
A Survey on Contextual Embeddings. CoRR (2020)
Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
Differentiable Causal Backdoor Discovery. AISTATS (2020)
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
Learning Binary Trees via Sparse Relaxation. CoRR (2020)
Niki Kilbertus, Matt J. Kusner, Ricardo Silva
A Class of Algorithms for General Instrumental Variable Models. CoRR (2020)
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)
Niki Kilbertus, Matt J. Kusner, Ricardo Silva
A Class of Algorithms for General Instrumental Variable Models. NeurIPS (2020)
Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
Differentiable Causal Backdoor Discovery. CoRR (2020)
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng
Gradient Regularized Budgeted Boosting. CoRR (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Generating Molecules via Chemical Reactions. DGS@ICLR (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. CoRR (2019)
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CCS (2019)
Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR (2019)
Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
Making Decisions that Reduce Discriminatory Impacts. ICML (2019)
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón
QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CoRR (2019)
Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris
Cumulo: A Dataset for Learning Cloud Classes. CoRR (2019)
Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI (2019)
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Model to Search for Synthesizable Molecules. NeurIPS (2019)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
A Generative Model For Electron Paths. ICLR (Poster) (2019)
Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva
Causal Reasoning for Algorithmic Fairness. CoRR (2018)
Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
Blind Justice: Fairness with Encrypted Sensitive Attributes. CoRR (2018)
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Predicting Electron Paths. CoRR (2018)
Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. ICML (2018)
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)
Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
Blind Justice: Fairness with Encrypted Sensitive Attributes. ICML (2018)
Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR (2018)
Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
Causal Interventions for Fairness. CoRR (2018)
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Grammar Variational Autoencoder. ICML (2017)
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 (2017)
Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
Counterfactual Fairness. CoRR (2017)
Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. NIPS (2017)
Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
Counterfactual Fairness. NIPS (2017)
Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
Supervised Word Mover's Distance. NIPS (2016)
Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Private Causal Inference. AISTATS (2016)
Matt J. Kusner, José Miguel Hernández-Lobato
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. CoRR (2016)
Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Fast Distributed k-Center Clustering with Outliers on Massive Data. NIPS (2015)
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 (2015)
Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
Differentially Private Bayesian Optimization. ICML (2015)
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger
From Word Embeddings To Document Distances. ICML (2015)
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) (2014)
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)
Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal
Stochastic Neighbor Compression. ICML (2014)
Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
Bayesian Optimization with Inequality Constraints. ICML (2014)
Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger
Stochastic Covariance Compression. CoRR (2014)
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
Cost-Sensitive Tree of Classifiers. ICML (1) (2013)
Zhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger
Anytime Representation Learning. ICML (3) (2013)
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
Cost-Sensitive Tree of Classifiers CoRR (2012)