1.1k citations · 1.1k across the 9 of their papers we have counts for
9 papers
Budgeted Multiple-Expert Deferral
Giulia DeSalvo, Clara Mohri, Mehryar Mohri +1
Learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems. However, standard traini…
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Ke Yu, Stephen Albro, Giulia DeSalvo +5
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure. Active lear…
Agile Modeling: From Concept to Classifier in Minutes
Otilia Stretcu, Edward Vendrow, Kenji Hata +15
The application of computer vision to nuanced subjective use cases is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a…
Leveraging Importance Weights in Subset Selection
Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar +3
We present a subset selection algorithm designed to work with arbitrary model families in a practical batch setting. In such a setting, an algorithm can sample examples one at a ti…
Batch Active Learning at Scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile +4
The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resourc…
Adaptive Region-Based Active Learning
Corinna Cortes, Giulia DeSalvo, Claudio Gentile +2
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region,…
Discrepancy-Based Algorithms for Non-Stationary Rested Bandits
Corinna Cortes, Giulia DeSalvo, Vitaly Kuznetsov +2
We study the multi-armed bandit problem where the rewards are realizations of general non-stationary stochastic processes, a setting that generalizes many existing lines of work an…
Online Learning with Abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile +2
We present an extensive study of the key problem of online learning where algorithms are allowed to abstain from making predictions. In the adversarial setting, we show how existin…
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo +2
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select c…