Anytime Representation Learning. - Zhixiang Eddie Xu; Matt J. Kusner; Gao Huang 001; Kilian Q. Weinberger (ICML (3), 2013)

Cost-Sensitive Tree of Classifiers. - Zhixiang Eddie Xu; Matt J. Kusner; Kilian Q. Weinberger; Minmin Chen (ICML (1), 2013)

Feature-Cost Sensitive Learning with Submodular Trees of Classifiers. - Matt J. Kusner; Wenlin Chen; Quan Zhou; Zhixiang Eddie Xu; Kilian Q. Weinberger; Yixin Chen 001 (AAAI, 2014)

Bayesian Optimization with Inequality Constraints. - Jacob R. Gardner; Matt J. Kusner; Zhixiang Eddie Xu; Kilian Q. Weinberger; John P. Cunningham (ICML, 2014)

Stochastic Neighbor Compression. - Matt J. Kusner; Stephen Tyree; Kilian Q. Weinberger; Kunal Agrawal 001 (ICML, 2014)

Stochastic Covariance Compression. - Matt J. Kusner; Nicholas I. Kolkin; Stephen Tyree; Kilian Q. Weinberger (CoRR, 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)

Differentially Private Bayesian Optimization. - Matt J. Kusner; Jacob R. Gardner; Roman Garnett; Kilian Q. Weinberger (ICML, 2015)

From Word Embeddings To Document Distances. - Matt J. Kusner; Yu Sun 020; Nicholas I. Kolkin; Kilian Q. Weinberger (ICML, 2015)

Fast Distributed k-Center Clustering with Outliers on Massive Data. - Gustavo Malkomes; Matt J. Kusner; Wenlin Chen; Kilian Q. Weinberger; Benjamin Moseley (NIPS, 2015)

Deep Manifold Traversal: Changing Labels with Convolutional Features. - Jacob R. Gardner; Matt J. Kusner; Yixuan Li 001; Paul Upchurch; Kilian Q. Weinberger; John E. Hopcroft (CoRR, 2015)

Private Causal Inference. - Matt J. Kusner; Yu Sun 020; Karthik Sridharan; Kilian Q. Weinberger (AISTATS, 2016)

Supervised Word Mover's Distance. - Gao Huang 001; Chuan Guo 001; Matt J. Kusner; Yu Sun 020; Fei Sha; Kilian Q. Weinberger (NIPS, 2016)

GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution. - Matt J. Kusner; José Miguel Hernández-Lobato (CoRR, 2016)

Grammar Variational Autoencoder. - Matt J. Kusner; Brooks Paige; José Miguel Hernández-Lobato (ICML, 2017)

Counterfactual Fairness. - Matt J. Kusner; Joshua R. Loftus; Chris Russell 001; Ricardo Silva 001 (NIPS, 2017)

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. - Chris Russell 001; Matt J. Kusner; Joshua R. Loftus; Ricardo Silva 001 (NIPS, 2017)

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 (ICLR (Poster), 2018)

Blind Justice: Fairness with Encrypted Sensitive Attributes. - Niki Kilbertus; Adrià Gascón; Matt J. Kusner; Michael Veale; Krishna P. Gummadi; Adrian Weller (ICML, 2018)

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. - Amartya Sanyal; Matt J. Kusner; Adrià Gascón; Varun Kanade (ICML, 2018)

Causal Reasoning for Algorithmic Fairness. - Joshua R. Loftus; Chris Russell 001; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2018)

Predicting Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (CoRR, 2018)

Causal Interventions for Fairness. - Matt J. Kusner; Chris Russell 001; Joshua R. Loftus; Ricardo Silva 001 (CoRR, 2018)

QUOTIENT: Two-Party Secure Neural Network Training and Prediction. - Nitin Agrawal 002; Ali Shahin Shamsabadi; Matt J. Kusner; Adrià Gascón (CCS, 2019)

A Generative Model For Electron Paths. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Generating Molecules via Chemical Reactions. - John Bradshaw; Matt J. Kusner; Brooks Paige; Marwin H. S. Segler; José Miguel Hernández-Lobato (DGS@ICLR, 2019)

Making Decisions that Reduce Discriminatory Impacts. - Matt J. Kusner; Chris Russell 001; Joshua R. Loftus; Ricardo Silva 001 (ICML, 2019)

A Model to Search for Synthesizable Molecules. - John Bradshaw; Brooks Paige; Matt J. Kusner; Marwin H. S. Segler; José Miguel Hernández-Lobato (NeurIPS, 2019)

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. - Niki Kilbertus; Philip J. Ball; Matt J. Kusner; Adrian Weller; Ricardo Silva 001 (UAI, 2019)

Gradient Regularized Budgeted Boosting. - Zhixiang Eddie Xu; Matt J. Kusner; Kilian Q. Weinberger; Alice X. Zheng (CoRR, 2019)

Cumulo: A Dataset for Learning Cloud Classes. - Valentina Zantedeschi; Fabrizio Falasca; Alyson Douglas; Richard Strange; Matt J. Kusner; Duncan Watson-Parris (CoRR, 2019)

Differentiable Causal Backdoor Discovery. - Limor Gultchin; Matt J. Kusner; Varun Kanade; Ricardo Silva 001 (AISTATS, 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 (NeurIPS, 2020)

A Class of Algorithms for General Instrumental Variable Models. - Niki Kilbertus; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2020)

A Survey on Contextual Embeddings. - Qi Liu 049; Matt J. Kusner; Phil Blunsom (CoRR, 2020)

Pre-Training by Completing Point Clouds. - Hanchen Wang 002; Qi Liu 049; Xiangyu Yue 001; Joan Lasenby; Matthew J. Kusner (CoRR, 2020)

Learning Binary Trees via Sparse Relaxation. - Valentina Zantedeschi; Matt J. Kusner; Vlad Niculae (CoRR, 2020)

Unsupervised Point Cloud Pre-training via Occlusion Completion. - Hanchen Wang 002; Qi Liu 049; Xiangyu Yue 001; Joan Lasenby; Matt J. Kusner (ICCV, 2021)

Operationalizing Complex Causes: A Pragmatic View of Mediation. - Limor Gultchin; David S. Watson; Matt J. Kusner; Ricardo Silva 001 (ICML, 2021)

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. - Afsaneh Mastouri; Yuchen Zhu; Limor Gultchin; Anna Korba; Ricardo Silva 001; Matt J. Kusner; Arthur Gretton; Krikamol Muandet (ICML, 2021)

Learning Binary Decision Trees by Argmin Differentiation. - Valentina Zantedeschi; Matt J. Kusner; Vlad Niculae (ICML, 2021)

Counterfactual Data Augmentation for Neural Machine Translation. - Qi Liu 049; Matt J. Kusner; Phil Blunsom (NAACL-HLT, 2021)

Causal Effect Inference for Structured Treatments. - Jean Kaddour; Yuchen Zhu; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2021)

Graph Intervention Networks for Causal Effect Estimation. - Jean Kaddour; Qi Liu 049; Yuchen Zhu; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2021)

Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022. - Awa Dieng; Miriam Rateike; Golnoosh Farnadi; Ferdinando Fioretto; Matt J. Kusner; Jessica Schrouff (AFCP, 2022)

When Do Flat Minima Optimizers Work? - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (NeurIPS, 2022)

Local Latent Space Bayesian Optimization over Structured Inputs. - Natalie Maus; Haydn Thomas Jones; Juston Moore; Matt J. Kusner; John Bradshaw; Jacob R. Gardner (NeurIPS, 2022)

Causal inference with treatment measurement error: a nonparametric instrumental variable approach. - Yuchen Zhu; Limor Gultchin; Arthur Gretton; Matt J. Kusner; Ricardo Silva 001 (UAI, 2022)

Questions for Flat-Minima Optimization of Modern Neural Networks. - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (CoRR, 2022)

Evaluating Self-Supervised Learning for Molecular Graph Embeddings. - Hanchen Wang 002; Jean Kaddour; Shengchao Liu; Jian Tang 005; Matt J. Kusner; Joan Lasenby; Qi Liu 049 (CoRR, 2022)

Causal Machine Learning: A Survey and Open Problems. - Jean Kaddour; Aengus Lynch; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2022)

MPC-Friendly Commitments for Publicly Verifiable Covert Security. - Nitin Agrawal 002; James Bell 001; Adrià Gascón; Matt J. Kusner (IACR Cryptol. ePrint Arch., 2022)

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 (AISTATS, 2023)

Stochastic Causal Programming for Bounding Treatment Effects. - Kirtan Padh; Jakob Zeitler; David S. Watson; Matt J. Kusner; Ricardo Silva 001; Niki Kilbertus (CLeaR, 2023)

DAG Learning on the Permutahedron. - Valentina Zantedeschi; Luca Franceschi 001; Jean Kaddour; Matt J. Kusner; Vlad Niculae (ICLR, 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 (NeurIPS, 2023)

Plasma Surrogate Modelling using Fourier Neural Operators. - 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; et al. (CoRR, 2023)

Proxy Methods for Domain Adaptation. - Katherine Tsai; Stephen R. Pfohl; Olawale Salaudeen; Nicole Chiou; Matt J. Kusner; Alexander D'Amour; Sanmi Koyejo; Arthur Gretton (AISTATS, 2024)

Valid Error Bars for Neural Weather Models using Conformal Prediction. - Vignesh Gopakumar; Joel Oskarrson; Ander Gray; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. - Vignesh Gopakumar; Ander Gray; Joel Oskarsson; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

Probabilistic Forecasting of Radiation Exposure for Spaceflight. - Rutuja Gurav; Elena Massara; Xiaomei Song; Kimberly Sinclair; Edward Brown; Matt J. Kusner; Bala Poduval; Atilim Gunes Baydin (CoRR, 2024)

When Can Proxies Improve the Sample Complexity of Preference Learning? - Yuchen Zhu; Daniel Augusto de Souza; Zhengyan Shi; Mengyue Yang; Pasquale Minervini; Alexander D'Amour; Matt J. Kusner (CoRR, 2024)

An Auditing Test to Detect Behavioral Shift in Language Models. - Leo Richter; Xuanli He; Pasquale Minervini; Matt J. Kusner (ICLR, 2025)

Setting the Record Straight on Transformer Oversmoothing. - Gbètondji J.-S. Dovonon; Michael M. Bronstein; Matt J. Kusner (Trans. Mach. Learn. Res., 2025)

Calibrated Physics-Informed Uncertainty Quantification. - Vignesh Gopakumar; Ander Gray; Lorenzo Zanisi; Timothy Nunn; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2025)