Matt J. Kusner

94 papers A* 26A 6Journal 49Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Agentic Uncertainty Reveals Agentic Overconfidence. CoRR Jean Kaddour, Srijan Patel, Gbètondji J.-S. Dovonon, Leo Richter, Pasquale Minervini, Matt J. Kusner
2026	J	`jnl`	Learning Physical Operators using Neural Operators. CoRR Vignesh Gopakumar, Ander Gray, Daniel Giles, Lorenzo Zanisi, Matt J. Kusner, Timo Betcke, Stanislas Pamela, Marc Peter Deisenroth
2025	A*	`conf`	An Auditing Test to Detect Behavioral Shift in Language Models. ICLR Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner
2025	A*	`conf`	Calibrated Physics-Informed Uncertainty Quantification. ICML Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth
2025	J	`jnl`	Calibrated Physics-Informed Uncertainty Quantification. CoRR Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2025	J	`jnl`	Causal Machine Learning: A Survey and Open Problems. Found. Trends Optim. Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
2025	J	`jnl`	Setting the Record Straight on Transformer Oversmoothing. Trans. Mach. Learn. Res. Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner
2025	A*	`conf`	When Can Proxies Improve the Sample Complexity of Preference Learning? ICML Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi, Mengyue Yang, Pasquale Minervini, Matt J. Kusner, Alexander Nicholas D'Amour
2024	J	`jnl`	An Auditing Test To Detect Behavioral Shift in Language Models. CoRR Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner
2024	J	`jnl`	Probabilistic Forecasting of Radiation Exposure for Spaceflight. CoRR Rutuja Gurav, Elena Massara, Xiaomei Song, Kimberly Sinclair, Edward Brown, Matt J. Kusner, Bala Poduval, Atilim Gunes Baydin
2024	A	`conf`	Proxy Methods for Domain Adaptation. AISTATS Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	Proxy Methods for Domain Adaptation. CoRR Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	Setting the Record Straight on Transformer Oversmoothing. CoRR Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner
2024	J	`jnl`	Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. CoRR Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2024	J	`jnl`	Valid Error Bars for Neural Weather Models using Conformal Prediction. CoRR Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2024	J	`jnl`	When Can Proxies Improve the Sample Complexity of Preference Learning? CoRR Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi, Mengyue Yang, Pasquale Minervini, Alexander D'Amour, Matt J. Kusner
2023	A	`conf`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2023	A*	`conf`	DAG Learning on the Permutahedron. ICLR Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
2023	J	`jnl`	DAG Learning on the Permutahedron. CoRR Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae
2023	A*	`conf`	No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. NeurIPS Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner
2023	J	`jnl`	No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. CoRR Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner
2023	J	`jnl`	Plasma Surrogate Modelling using Fourier Neural Operators. CoRR Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt J. Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team
2023	—	`conf`	Stochastic Causal Programming for Bounding Treatment Effects. CLeaR Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
2022	J	`jnl`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2022	—	`conf`	Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022. AFCP Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff
2022	—	`ed.`	Algorithmic Fairness through the Lens of Causality and Privacy Workshop, AFCP 2022, New Orleans, LA, USA (hybrid), 03 December 2022 AFCP Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff
2022	J	`jnl`	Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	J	`jnl`	Causal Machine Learning: A Survey and Open Problems. CoRR Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
2022	A	`conf`	Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	J	`jnl`	Evaluating Self-Supervised Learning for Molecular Graph Embeddings. CoRR Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu
2022	A*	`conf`	Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS Natalie Maus, Haydn Thomas Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
2022	J	`jnl`	Local Latent Space Bayesian Optimization over Structured Inputs. CoRR Natalie Maus, Haydn Thomas Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
2022	J	`jnl`	MPC-Friendly Commitments for Publicly Verifiable Covert Security. IACR Cryptol. ePrint Arch. Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
2022	J	`jnl`	Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
2022	J	`jnl`	Stochastic Causal Programming for Bounding Treatment Effects. CoRR Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
2022	A*	`conf`	When Do Flat Minima Optimizers Work? NeurIPS Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
2021	A*	`conf`	Causal Effect Inference for Structured Treatments. NeurIPS Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva
2021	—	`conf`	Counterfactual Data Augmentation for Neural Machine Translation. NAACL-HLT Qi Liu, Matt J. Kusner, Phil Blunsom
2021	J	`jnl`	Graph Intervention Networks for Causal Effect Estimation. CoRR Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva
2021	A*	`conf`	Learning Binary Decision Trees by Argmin Differentiation. ICML Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
2021	A*	`conf`	MPC-Friendly Commitments for Publicly Verifiable Covert Security. CCS Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
2021	J	`jnl`	MPC-Friendly Commitments for Publicly Verifiable Covert Security. CoRR Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
2021	A*	`conf`	Operationalizing Complex Causes: A Pragmatic View of Mediation. ICML Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
2021	J	`jnl`	Operationalizing Complex Causes: A Pragmatic View of Mediation. CoRR Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
2021	A*	`conf`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	J	`jnl`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	A*	`conf`	Unsupervised Point Cloud Pre-training via Occlusion Completion. ICCV Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, Matt J. Kusner
2020	A*	`conf`	A Class of Algorithms for General Instrumental Variable Models. NeurIPS Niki Kilbertus, Matt J. Kusner, Ricardo Silva
2020	J	`jnl`	A Class of Algorithms for General Instrumental Variable Models. CoRR Niki Kilbertus, Matt J. Kusner, Ricardo Silva
2020	J	`jnl`	A Survey on Contextual Embeddings. CoRR Qi Liu, Matt J. Kusner, Phil Blunsom
2020	A*	`conf`	Barking up the right tree: an approach to search over molecule synthesis DAGs. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	J	`jnl`	Barking up the right tree: an approach to search over molecule synthesis DAGs. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2020	A	`conf`	Differentiable Causal Backdoor Discovery. AISTATS Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
2020	J	`jnl`	Differentiable Causal Backdoor Discovery. CoRR Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
2020	J	`jnl`	Learning Binary Trees via Sparse Relaxation. CoRR Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
2019	—	`conf`	A Generative Model For Electron Paths. ICLR (Poster) John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	A*	`conf`	A Model to Search for Synthesizable Molecules. NeurIPS John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	A Model to Search for Synthesizable Molecules. CoRR John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	Cumulo: A Dataset for Learning Cloud Classes. CoRR Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris
2019	—	`conf`	Generating Molecules via Chemical Reactions. DGS@ICLR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2019	J	`jnl`	Gradient Regularized Budgeted Boosting. CoRR Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng
2019	A*	`conf`	Making Decisions that Reduce Discriminatory Impacts. ICML Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
2019	A*	`conf`	QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CCS Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón
2019	J	`jnl`	QUOTIENT: Two-Party Secure Neural Network Training and Prediction. CoRR Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón
2019	A	`conf`	The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
2019	J	`jnl`	The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
2018	A*	`conf`	Blind Justice: Fairness with Encrypted Sensitive Attributes. ICML Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
2018	J	`jnl`	Blind Justice: Fairness with Encrypted Sensitive Attributes. CoRR Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller
2018	J	`jnl`	Causal Interventions for Fairness. CoRR Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
2018	J	`jnl`	Causal Reasoning for Algorithmic Fairness. CoRR Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva
2018	—	`conf`	Learning a Generative Model for Validity in Complex Discrete Structures. ICLR (Poster) David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2018	J	`jnl`	Predicting Electron Paths. CoRR John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
2018	A*	`conf`	TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. ICML Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
2018	J	`jnl`	TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade
2017	—	`conf`	Counterfactual Fairness. NIPS Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
2017	J	`jnl`	Counterfactual Fairness. CoRR Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
2017	A*	`conf`	Grammar Variational Autoencoder. ICML Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
2017	J	`jnl`	Learning a Generative Model for Validity in Complex Discrete Structures. CoRR David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
2017	—	`conf`	When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. NIPS Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva
2016	J	`jnl`	GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. CoRR Matt J. Kusner, José Miguel Hernández-Lobato
2016	A	`conf`	Private Causal Inference. AISTATS Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
2016	—	`conf`	Supervised Word Mover's Distance. NIPS Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
2015	J	`jnl`	Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft
2015	A*	`conf`	Differentially Private Bayesian Optimization. ICML Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
2015	—	`conf`	Fast Distributed k-Center Clustering with Outliers on Massive Data. NIPS Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
2015	A*	`conf`	From Word Embeddings To Document Distances. ICML Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger
2014	A*	`conf`	Bayesian Optimization with Inequality Constraints. ICML Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
2014	J	`jnl`	Classifier cascades and trees for minimizing feature evaluation cost. J. Mach. Learn. Res. Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle
2014	A*	`conf`	Feature-Cost Sensitive Learning with Submodular Trees of Classifiers. AAAI Matt J. Kusner, Wenlin Chen, Quan Zhou, Zhixiang Eddie Xu, Kilian Q. Weinberger, Yixin Chen
2014	J	`jnl`	Stochastic Covariance Compression. CoRR Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger
2014	A*	`conf`	Stochastic Neighbor Compression. ICML Matt J. Kusner, Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal
2013	—	`conf`	Anytime Representation Learning. ICML (3) Zhixiang Eddie Xu, Matt J. Kusner, Gao Huang, Kilian Q. Weinberger
2013	—	`conf`	Cost-Sensitive Tree of Classifiers. ICML (1) Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
2012	J	`jnl`	Cost-Sensitive Tree of Classifiers CoRR Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen