NewEvery arXiv paper, its researchers & institutions — mapped.
papers

Publications (43)

cs.LG2021

Learning Binary Decision Trees by Argmin Differentiation

Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

cs.LG2019

Gradient Regularized Budgeted Boosting

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger +1

cs.LG2020

Barking up the right tree: an approach to search over molecule synthesis DAGs

John Bradshaw, Brooks Paige, Matt J. Kusner +2

cs.CL2020

A Survey on Contextual Embeddings

Qi Liu, Matt J. Kusner, Phil Blunsom

stat.ML2018

Blind Justice: Fairness with Encrypted Sensitive Attributes

Niki Kilbertus, Adrià Gascón, Matt J. Kusner +3

cs.LG2023

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin +5

stat.ML2018

Causal Interventions for Fairness

Matt J. Kusner, Chris Russell, Joshua R. Loftus +1

stat.ML2016

Private Causal Inference

Matt J. Kusner, Yu Sun, Karthik Sridharan +1

stat.ML2013

Cost-Sensitive Tree of Classifiers

Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger +1

cs.LG2025

Calibrated Physics-Informed Uncertainty Quantification

Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi +5

cs.LG2021

Operationalizing Complex Causes: A Pragmatic View of Mediation

Limor Gultchin, David S. Watson, Matt J. Kusner +1

stat.ML2016

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

Matt J. Kusner, José Miguel Hernández-Lobato

cs.LG2023

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

Jean Kaddour, Oscar Key, Piotr Nawrot +2

cs.LG2016

Deep Manifold Traversal: Changing Labels with Convolutional Features

Jacob R. Gardner, Paul Upchurch, Matt J. Kusner +4

cs.AI2026

Uncertainty Quantification of Surrogate Models using Conformal Prediction

Vignesh Gopakumar, Ander Gray, Joel Oskarsson +5

cs.LG2025

An Auditing Test To Detect Behavioral Shift in Language Models

Leo Richter, Xuanli He, Pasquale Minervini +1

stat.ML2018

Counterfactual Fairness

Matt J. Kusner, Joshua R. Loftus, Chris Russell +1

cs.LG2021

Causal Effect Inference for Structured Treatments

Jean Kaddour, Yuchen Zhu, Qi Liu +2

stat.ML2015

Differentially Private Bayesian Optimization

Matt J. Kusner, Jacob R. Gardner, Roman Garnett +1

stat.ML2015

Image Data Compression for Covariance and Histogram Descriptors

Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree +1

physics.ao-ph2022

Cumulo: A Dataset for Learning Cloud Classes

Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas +3

cs.CR2022

MPC-Friendly Commitments for Publicly Verifiable Covert Security

Nitin Agrawal, James Bell, Adrià Gascón +1

cs.LG2020

Differentiable Causal Backdoor Discovery

Limor Gultchin, Matt J. Kusner, Varun Kanade +1

cs.LG2026

Learning Physical Operators using Neural Operators

Vignesh Gopakumar, Ander Gray, Dan Giles +5

cs.LG2024

Setting the Record Straight on Transformer Oversmoothing

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

cs.LG2024

Proxy Methods for Domain Adaptation

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen +5

cs.CR2018

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

Amartya Sanyal, Matt J. Kusner, Adrià Gascón +1

cs.AI2026

Agentic Uncertainty Reveals Agentic Overconfidence

Jean Kaddour, Srijan Patel, Gbètondji Dovonon +3

cs.LG2023

When Do Flat Minima Optimizers Work?

Jean Kaddour, Linqing Liu, Ricardo Silva +1

cs.CR2019

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner +1

cs.CR2026

Plausible Deniability Guarantees for Whistleblowers

Leo Richter, Matt J. Kusner

The paper proposes formal privacy guarantees for whistleblowers by applying per-report (0,δ)-differential privacy to audit selection transcripts, using a reduction to private conti…

#whistleblower protection#differential privacy#private auditing#continual counting
stat.ML2017

Grammar Variational Autoencoder

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

cs.LG2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Niki Kilbertus, Philip J. Ball, Matt J. Kusner +2

cs.LG2026

Causal Machine Learning: A Survey and Open Problems

Jean Kaddour, Aengus Lynch, Qi Liu +2

cs.LG2020

A Class of Algorithms for General Instrumental Variable Models

Niki Kilbertus, Matt J. Kusner, Ricardo Silva

stat.ML2018

Learning a Generative Model for Validity in Complex Discrete Structures

David Janz, Jos van der Westhuizen, Brooks Paige +2

stat.ML2022

Adapting to Latent Subgroup Shifts via Concepts and Proxies

Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour +7

cs.LG2023

Local Latent Space Bayesian Optimization over Structured Inputs

Natalie Maus, Haydn T. Jones, Juston S. Moore +3

cs.LG2023

DAG Learning on the Permutahedron

Valentina Zantedeschi, Luca Franceschi, Jean Kaddour +2

physics.chem-ph2019

A Generative Model For Electron Paths

John Bradshaw, Matt J. Kusner, Brooks Paige +2

cs.AI2018

Causal Reasoning for Algorithmic Fairness

Joshua R. Loftus, Chris Russell, Matt J. Kusner +1

cs.LG2024

When Can Proxies Improve the Sample Complexity of Preference Learning?

Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi +4

cs.LG2019

A Model to Search for Synthesizable Molecules

John Bradshaw, Brooks Paige, Matt J. Kusner +2