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Giulia DeSalvo

16 accepted papers

2025

GIST: Greedy Independent Set Thresholding for Max-Min Diversification with Submodular Utility

Matthew Fahrbach, Srikumar Ramalingam, Morteza Zadimoghaddam, Sara Ahmadian, Gui Citovsky, Giulia DeSalvo
NeurIPS 2025poster

This work studies a novel subset selection problem called *max-min diversification with monotone submodular utility* (MDMS), which has a wide range of applications in machine learning, e.g., data sampling and feature selection. Given a set of points in a metric space, the goal of MDMS is to maximize…

Cited by 0Source ↗Scholar ↗
2023

Agile Modeling: From Concept to Classifier in Minutes

Otilia Stretcu, Edward Vendrow, Kenji Hata, Krishnamurthy Viswanathan, Vittorio Ferrari, Sasan Tavakkol +12
ICCV 2023poster

The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "…

Cited by 14PDF ↗Scholar ↗
2023

Leveraging Importance Weights in Subset Selection

Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang
ICLR 2023poster

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 time but, in order to limit overhead costs, is only able to update its state (i.e. further train model weights) once a lar…

Cited by 3Source ↗PDF ↗Scholar ↗
2022

Firebolt: Weak Supervision Under Weaker Assumptions

Zhaobin Kuang, Chidubem G. Arachie, Bangyong Liang, Pradyumna Narayana, Giulia Desalvo, Michael S. Quinn +3
AISTATS 2022poster

Modern machine learning demands a large amount of training data. Weak supervision is a promising approach to meet this demand. It aggregates multiple labeling functions (LFs)–noisy, user-provided labeling heuristics—to rapidly and cheaply curate probabilistic labels for large-scale unlabeled data. H…

Cited by 11Source ↗PDF ↗Scholar ↗
2021

Batch Active Learning at Scale

Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh +1
NeurIPS 2021poster

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 resources. Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addres…

Cited by 189Source ↗PDF ↗Scholar ↗
2020

Adaptive Region-Based Active Learning

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
ICML 2020poster

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, while actively requesting labels. We prove theoretical guarantees for both the generalization error and the label comple…

Cited by 16Source ↗PDF ↗Scholar ↗
2020

Online Learning with Dependent Stochastic Feedback Graphs

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
ICML 2020poster

A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner. We study a challenging scenario where feedback graphs vary stochastically with time and, more importantly, where graphs and losses are dependent. This sc…

Cited by 18Source ↗PDF ↗Scholar ↗
2020

Understanding the Effects of Batching in Online Active Learning

Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh
AISTATS 2020poster

Online active learning (AL) algorithms often assume immediate access to a label once a query has been made. However, due to practical constraints, the labels of these queried examples are generally only available in “batches”. In this work, we present an analysis for a generic class of batch online…

Cited by 9Source ↗PDF ↗Scholar ↗
2019

Active Learning with Disagreement Graphs

Corinna Cortes, Giulia Desalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile
ICML 2019oral

We present two novel enhancements of an online importance-weighted active learning algorithm IWAL, using the properties of disagreements among hypotheses. The first enhancement, IWALD, prunes the hypothesis set with a more aggressive strategy based on the disagreement graph. We show that IWAL-D impr…

Cited by 28Source ↗PDF ↗Scholar ↗
2019

Online Learning with Sleeping Experts and Feedback Graphs

Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Scott Yang
ICML 2019oral

We consider the scenario of online learning with sleeping experts, where not all experts are available at each round, and analyze the general framework of learning with feedback graphs, where the loss observations associated with each expert are characterized by a graph. A critical assumption in thi…

Cited by 20Source ↗PDF ↗Scholar ↗
2017

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization

Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
ICLR 2017poster

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian Optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation. We present Hyperband, a no…

Cited by 196Source ↗Scholar ↗