Matt J. Kusner

Blind Justice: Fairness with Encrypted Sensitive Attributes

2018 · arXiv (Cornell University)

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. To avoid disparate treatment, sensitive …

Learning Binary Decision Trees by Argmin Differentiation

2020 · arXiv (Cornell University)

We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node pruning) and continuous parameters (i.e., for tree …

Local Latent Space Bayesian Optimization over Structured Inputs

2022 · arXiv (Cornell University)

Bayesian optimization over the latent spaces of deep autoencoder models (DAEs) has recently emerged as a promising new approach for optimizing challenging black-box functions over structured, discrete, hard-to-enumerate search spaces (e.g., molecules). Here the DAE …

Supervised word mover's distance

2016 · PolyPublie (École Polytechnique de Montréal)

Recently, a new document metric called the word mover’s distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance …

From Word Embeddings To Document Distances

2015 · PolyPublie (École Polytechnique de Montréal)

We present the Word Mover's Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representations for words from local cooccurrences in …