Matt J. Kusner

According to our database, Matt J. Kusner authored at least 60 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

An Auditing Test To Detect Behavioral Shift in Language Models.

Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner

CoRR, 2024

Setting the Record Straight on Transformer Oversmoothing.

Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner

CoRR, 2024

Proxy Methods for Domain Adaptation.

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models.

Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DAG Learning on the Permutahedron.

Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Stochastic Causal Programming for Bounding Treatment Effects.

Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus

Proceedings of the Conference on Causal Learning and Reasoning, 2023

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

MPC-Friendly Commitments for Publicly Verifiable Covert Security.

Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner

IACR Cryptol. ePrint Arch., 2022

Causal Machine Learning: A Survey and Open Problems.

Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva

CoRR, 2022

Evaluating Self-Supervised Learning for Molecular Graph Embeddings.

Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu

CoRR, 2022

Questions for Flat-Minima Optimization of Modern Neural Networks.

Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

CoRR, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.

Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner

CoRR, 2022

Causal inference with treatment measurement error: a nonparametric instrumental variable approach.

Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.

Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

When Do Flat Minima Optimizers Work?

Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022.

Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff

Proceedings of the Algorithmic Fairness through the Lens of Causality and Privacy Workshop, 2022

2021

Graph Intervention Networks for Causal Effect Estimation.

Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva

CoRR, 2021

Causal Effect Inference for Structured Treatments.

Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Counterfactual Data Augmentation for Neural Machine Translation.

Qi Liu, Matt J. Kusner, Phil Blunsom

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Learning Binary Decision Trees by Argmin Differentiation.

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Operationalizing Complex Causes: A Pragmatic View of Mediation.

Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

Proceedings of the 38th International Conference on Machine Learning, 2021

Unsupervised Point Cloud Pre-training via Occlusion Completion.

Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt J. Kusner

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Learning Binary Trees via Sparse Relaxation.

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

CoRR, 2020

Pre-Training by Completing Point Clouds.

Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matthew J. Kusner

CoRR, 2020

A Survey on Contextual Embeddings.

Qi Liu, Matt J. Kusner, Phil Blunsom

CoRR, 2020

A Class of Algorithms for General Instrumental Variable Models.

Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Differentiable Causal Backdoor Discovery.

Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Cumulo: A Dataset for Learning Cloud Classes.

Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris

CoRR, 2019

Gradient Regularized Budgeted Boosting.

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng

CoRR, 2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.

Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

A Model to Search for Synthesizable Molecules.

John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Making Decisions that Reduce Discriminatory Impacts.

Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Proceedings of the 36th International Conference on Machine Learning, 2019

Generating Molecules via Chemical Reactions.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

A Generative Model For Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 2019

QUOTIENT: Two-Party Secure Neural Network Training and Prediction.

Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

2018

Causal Interventions for Fairness.

Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

CoRR, 2018

Predicting Electron Paths.

John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

CoRR, 2018

Causal Reasoning for Algorithmic Fairness.

Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

CoRR, 2018

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service.

Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade

Proceedings of the 35th International Conference on Machine Learning, 2018

Blind Justice: Fairness with Encrypted Sensitive Attributes.

Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.

Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Counterfactual Fairness.

Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Grammar Variational Autoencoder.

Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution.

Matt J. Kusner, José Miguel Hernández-Lobato

CoRR, 2016

Supervised Word Mover's Distance.

Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Private Causal Inference.

Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Deep Manifold Traversal: Changing Labels with Convolutional Features.

Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft

CoRR, 2015

Fast Distributed k-Center Clustering with Outliers on Massive Data.

Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

From Word Embeddings To Document Distances.

Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger

Proceedings of the 32nd International Conference on Machine Learning, 2015

Differentially Private Bayesian Optimization.

Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Classifier cascades and trees for minimizing feature evaluation cost.

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle

J. Mach. Learn. Res., 2014

Stochastic Covariance Compression.

Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger

CoRR, 2014

Stochastic Neighbor Compression.

Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal

Proceedings of the 31th International Conference on Machine Learning, 2014

Bayesian Optimization with Inequality Constraints.

Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham

Proceedings of the 31th International Conference on Machine Learning, 2014

Feature-Cost Sensitive Learning with Submodular Trees of Classifiers.

Matt J. Kusner, Wenlin Chen, Quan Zhou, Zhixiang Eddie Xu, Kilian Q. Weinberger, Yixin Chen

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Cost-Sensitive Tree of Classifiers.

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen

Proceedings of the 30th International Conference on Machine Learning, 2013

Anytime Representation Learning.

Zhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger

Proceedings of the 30th International Conference on Machine Learning, 2013