Search Results for author: Matt J. Kusner

Found 37 papers, 19 papers with code

DAG Learning on the Permutahedron

1 code implementation27 Jan 2023 Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae

We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data.

Adapting to Latent Subgroup Shifts via Concepts and Proxies

no code implementations21 Dec 2022 Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai

We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.

Unsupervised Domain Adaptation

Causal Machine Learning: A Survey and Open Problems

no code implementations30 Jun 2022 Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).

BIG-bench Machine Learning Fairness +1

When Do Flat Minima Optimizers Work?

1 code implementation1 Feb 2022 Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.

Benchmarking Graph Learning +9

Local Latent Space Bayesian Optimization over Structured Inputs

1 code implementation28 Jan 2022 Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner

By reformulating the encoder to function as both an encoder for the DAE globally and as a deep kernel for the surrogate model within a trust region, we better align the notion of local optimization in the latent space with local optimization in the input space.

MPC-Friendly Commitments for Publicly Verifiable Covert Security

no code implementations15 Sep 2021 Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner

We address the problem of efficiently verifying a commitment in a two-party computation.

Operationalizing Complex Causes: A Pragmatic View of Mediation

1 code implementation9 Jun 2021 Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).

Causal Effect Inference for Structured Treatments

2 code implementations NeurIPS 2021 Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva

We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).

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

2 code implementations10 May 2021 Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.

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

1 code implementation NeurIPS 2020 John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

When designing new molecules with particular properties, it is not only important what to make but crucially how to make it.

Learning Binary Decision Trees by Argmin Differentiation

1 code implementation9 Oct 2020 Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae

We address the problem of learning binary decision trees that partition data for some downstream task.

A Class of Algorithms for General Instrumental Variable Models

1 code implementation NeurIPS 2020 Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.

A Survey on Contextual Embeddings

no code implementations16 Mar 2020 Qi Liu, Matt J. Kusner, Phil Blunsom

Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language processing tasks.

Model Compression

Differentiable Causal Backdoor Discovery

1 code implementation3 Mar 2020 Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Discovering the causal effect of a decision is critical to nearly all forms of decision-making.

Decision Making

Cumulo: A Dataset for Learning Cloud Classes

1 code implementation5 Nov 2019 Valentina Zantedeschi, Fabrizio Falasca, Alyson Douglas, Richard Strange, Matt J. Kusner, Duncan Watson-Parris

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

no code implementations8 Jul 2019 Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón

In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts.

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

1 code implementation1 Jul 2019 Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.


A Model to Search for Synthesizable Molecules

1 code implementation NeurIPS 2019 John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.

Generating Molecules via Chemical Reactions

no code implementations ICLR Workshop DeepGenStruct 2019 John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.

Gradient Regularized Budgeted Boosting

no code implementations13 Jan 2019 Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng

As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial.

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

1 code implementation ICML 2018 Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade

The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data.

BIG-bench Machine Learning Binarization

Blind Justice: Fairness with Encrypted Sensitive Attributes

1 code implementation ICML 2018 Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller

Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.


Causal Interventions for Fairness

no code implementations6 Jun 2018 Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.


Causal Reasoning for Algorithmic Fairness

no code implementations15 May 2018 Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.

Decision Making Fairness

Learning a Generative Model for Validity in Complex Discrete Structures

1 code implementation ICLR 2018 David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato

This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences -- and thus faithfully model discrete objects.

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

no code implementations NeurIPS 2017 Chris Russell, Matt J. Kusner, Joshua Loftus, Ricardo Silva

In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification.

Counterfactual Inference Fairness

Counterfactual Fairness

2 code implementations NeurIPS 2017 Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.

BIG-bench Machine Learning Causal Inference +1

Supervised Word Mover's Distance

1 code implementation NeurIPS 2016 Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger

Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.

Document Classification General Classification +2

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

no code implementations12 Nov 2016 Matt J. Kusner, José Miguel Hernández-Lobato

Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements.

Private Causal Inference

no code implementations17 Dec 2015 Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger

Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few.

Causal Inference

Deep Manifold Traversal: Changing Labels with Convolutional Features

no code implementations19 Nov 2015 Jacob R. Gardner, Paul Upchurch, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft

Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership.

Differentially Private Bayesian Optimization

no code implementations16 Jan 2015 Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger

The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems.

BIG-bench Machine Learning

Image Data Compression for Covariance and Histogram Descriptors

no code implementations4 Dec 2014 Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger

Specifically, we show that we can reduce data sets to 16% and in some cases as little as 2% of their original size, while approximately matching the test error of kNN classification on the full training set.

Data Compression General Classification

Cost-Sensitive Tree of Classifiers

no code implementations9 Oct 2012 Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen

Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e. g. search engines and email spam filters).

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