Matt J. Kusner

research

∙ 07/12/2023

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

The computation necessary for training Transformer-based language models...

0 Jean Kaddour, et al. ∙

research

∙ 01/27/2023

DAG Learning on the Permutahedron

We propose a continuous optimization framework for discovering a latent ...

0 Valentina Zantedeschi, et al. ∙

research

∙ 12/21/2022

Adapting to Latent Subgroup Shifts via Concepts and Proxies

We address the problem of unsupervised domain adaptation when the source...

1 Ibrahim Alabdulmohsin, et al. ∙

research

∙ 01/28/2022

Local Latent Space Bayesian Optimization over Structured Inputs

Bayesian optimization over the latent spaces of deep autoencoder models ...

0 Natalie Maus, et al. ∙

research

∙ 09/15/2021

MPC-Friendly Commitments for Publicly Verifiable Covert Security

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

0 Nitin Agrawal, et al. ∙

research

∙ 12/21/2020

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

When designing new molecules with particular properties, it is not only ...

11 John Bradshaw, et al. ∙

research

∙ 10/09/2020

Learning Binary Trees via Sparse Relaxation

One of the most classical problems in machine learning is how to learn b...

0 Valentina Zantedeschi, et al. ∙

research

∙ 06/11/2020

A Class of Algorithms for General Instrumental Variable Models

Causal treatment effect estimation is a key problem that arises in a var...

12 Niki Kilbertus, et al. ∙

research

∙ 03/16/2020

A Survey on Contextual Embeddings

Contextual embeddings, such as ELMo and BERT, move beyond global word re...

38 Qi Liu, et al. ∙

research

∙ 03/03/2020

Differentiable Causal Backdoor Discovery

Discovering the causal effect of a decision is critical to nearly all fo...

0 Limor Gultchin, et al. ∙

research

∙ 11/05/2019

Cumulo: A Dataset for Learning Cloud Classes

One of the greatest sources of uncertainty in future climate projections...

0 Valentina Zantedeschi, et al. ∙

research

∙ 07/08/2019

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of sec...

0 Nitin Agrawal, et al. ∙

research

∙ 07/01/2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Causal approaches to fairness have seen substantial recent interest, bot...

5 Niki Kilbertus, et al. ∙

research

∙ 06/12/2019

A Model to Search for Synthesizable Molecules

Deep generative models are able to suggest new organic molecules by gene...

7 John Bradshaw, et al. ∙

research

∙ 01/13/2019

Gradient Regularized Budgeted Boosting

As machine learning transitions increasingly towards real world applicat...

0 Zhixiang Eddie Xu, et al. ∙

research

∙ 06/09/2018

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

Machine learning methods are widely used for a variety of prediction pro...

0 Amartya Sanyal, et al. ∙

research

∙ 06/08/2018

Blind Justice: Fairness with Encrypted Sensitive Attributes

Recent work has explored how to train machine learning models which do n...

0 Niki Kilbertus, et al. ∙

research

∙ 06/06/2018

Causal Interventions for Fairness

Most approaches in algorithmic fairness constrain machine learning metho...

0 Matt J. Kusner, et al. ∙

research

∙ 05/23/2018

Predicting Electron Paths

Chemical reactions can be described as the stepwise redistribution of el...

0 John Bradshaw, et al. ∙

research

∙ 05/15/2018

Causal Reasoning for Algorithmic Fairness

In this work, we argue for the importance of causal reasoning in creatin...

0 Joshua R. Loftus, et al. ∙

research

∙ 12/05/2017

Learning a Generative Model for Validity in Complex Discrete Structures

Deep generative models have been successfully used to learn representati...

0 David Janz, et al. ∙

research

∙ 03/20/2017

Counterfactual Fairness

Machine learning can impact people with legal or ethical consequences wh...

0 Matt J. Kusner, et al. ∙

research

∙ 03/06/2017

Grammar Variational Autoencoder

Deep generative models have been wildly successful at learning coherent ...

0 Matt J. Kusner, et al. ∙

research

∙ 11/12/2016

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

Generative Adversarial Networks (GAN) have limitations when the goal is ...

0 Matt J. Kusner, et al. ∙

research

∙ 12/17/2015

Private Causal Inference

Causal inference deals with identifying which random variables "cause" o...

0 Matt J. Kusner, et al. ∙

research

∙ 11/19/2015

Deep Manifold Traversal: Changing Labels with Convolutional Features

Many tasks in computer vision can be cast as a "label changing" problem,...

0 Jacob R. Gardner, et al. ∙

research

∙ 01/16/2015

Differentially Private Bayesian Optimization

Bayesian optimization is a powerful tool for fine-tuning the hyper-param...

0 Matt J. Kusner, et al. ∙

research

∙ 12/04/2014

Image Data Compression for Covariance and Histogram Descriptors

Covariance and histogram image descriptors provide an effective way to c...

0 Matt J. Kusner, et al. ∙

research

∙ 10/09/2012

Cost-Sensitive Tree of Classifiers

Recently, machine learning algorithms have successfully entered large-sc...

0 Zhixiang Xu, et al. ∙

Matt J. Kusner

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