8 citations · 31 across the 13 of their papers we have counts for
5 papers · 1 filter
Double Adaptive Stochastic Gradient Optimization
Kin Gutierrez, Jin Li, Cristian Challu +1
Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of a…
Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises w…
On the Interaction Effects Between Prediction and Clustering
Matt Barnes, Artur Dubrawski
Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions. This paper argues interaction effects betw…
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu, Sivaraman Balakrishnan, Aarti Singh +1
In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain a…
Novel Prediction Techniques Based on Clusterwise Linear Regression
Igor Gitman, Jieshi Chen, Eric Lei +1
In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into clusters, such that linear re…