Giulia DeSalvo

2.7k total citations · 1 hit paper
14 papers, 598 citations indexed

About

Giulia DeSalvo is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Giulia DeSalvo has authored 14 papers receiving a total of 598 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 6 papers in Management Science and Operations Research and 3 papers in Computer Networks and Communications. Recurrent topics in Giulia DeSalvo's work include Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (7 papers) and Advanced Bandit Algorithms Research (6 papers). Giulia DeSalvo is often cited by papers focused on Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (7 papers) and Advanced Bandit Algorithms Research (6 papers). Giulia DeSalvo collaborates with scholars based in United States, Switzerland and China. Giulia DeSalvo's co-authors include Afshin Rostamizadeh, Kevin Jamieson, Ameet Talwalkar, Lisha Li, Mehryar Mohri, Corinna Cortes, Claudio Gentile, Scott Cheng‐Hsin Yang, Ariel Fuxman and Krishnamurthy Viswanathan and has published in prestigious journals such as Journal of Machine Learning Research, Annals of Mathematics and Artificial Intelligence and arXiv (Cornell University).

In The Last Decade

Giulia DeSalvo

12 papers receiving 565 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Hyperband: a novel bandit-based approach to hyperparameter optimization

2017 510 citations Lisha Li, Kevin Jamieson et al. Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Giulia DeSalvo United States	6	306	114	55	46	44	14	598
André Luis Debiaso Rossi Brazil	11	361 1.2×	100 0.9×	69 1.3×	49 1.1×	45 1.0×	32	720
Haifeng Jin China	10	372 1.2×	168 1.5×	64 1.2×	32 0.7×	36 0.8×	27	708
Ying Cao China	8	221 0.7×	84 0.7×	34 0.6×	58 1.3×	31 0.7×	17	728
Rakesh Katuwal Singapore	9	368 1.2×	162 1.4×	99 1.8×	40 0.9×	28 0.6×	10	590
Hanen Borchani Spain	6	200 0.7×	85 0.7×	43 0.8×	27 0.6×	30 0.7×	8	506
Jin Gou China	14	255 0.8×	84 0.7×	49 0.9×	31 0.7×	75 1.7×	58	603
Youssef Ghanou Morocco	9	278 0.9×	81 0.7×	62 1.1×	33 0.7×	19 0.4×	21	649
Hongfang Zhou China	8	241 0.8×	108 0.9×	39 0.7×	18 0.4×	41 0.9×	21	471
Frauke Friedrichs Germany	5	218 0.7×	90 0.8×	47 0.9×	23 0.5×	40 0.9×	7	421

Countries citing papers authored by Giulia DeSalvo

Since Specialization

Citations

This map shows the geographic impact of Giulia DeSalvo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Giulia DeSalvo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulia DeSalvo more than expected).

Fields of papers citing papers by Giulia DeSalvo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giulia DeSalvo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Giulia DeSalvo. The network helps show where Giulia DeSalvo may publish in the future.

Co-authorship network of co-authors of Giulia DeSalvo

This figure shows the co-authorship network connecting the top 25 collaborators of Giulia DeSalvo. A scholar is included among the top collaborators of Giulia DeSalvo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Giulia DeSalvo. Giulia DeSalvo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

1.

Cortes, Corinna, Giulia DeSalvo, & Mehryar Mohri. (2023). Theory and algorithms for learning with rejection in binary classification. Annals of Mathematics and Artificial Intelligence. 92(2). 277–315.

2.

Stretcu, Otilia, Krishnamurthy Viswanathan, Vittorio Ferrari, et al.. (2023). Agile Modeling: From Concept to Classifier in Minutes. 22266–22277. 5 indexed citations

3.

DeSalvo, Giulia, et al.. (2021). Online Active Learning with Surrogate Loss Functions. Neural Information Processing Systems. 34. 1 indexed citations

4.

Cortes, Corinna, et al.. (2020). Online Learning with Dependent Stochastic Feedback Graphs. International Conference on Machine Learning. 1. 2154–2163. 2 indexed citations

5.

Cortes, Corinna, et al.. (2020). Adaptive Region-Based Active Learning. International Conference on Machine Learning. 1. 2144–2153. 2 indexed citations

6.

Cortes, Corinna, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, & Scott Cheng‐Hsin Yang. (2019). Online Learning with Sleeping Experts and Feedback Graphs. International Conference on Machine Learning. 1370–1378. 5 indexed citations

7.

Cortes, Corinna, et al.. (2019). Region-Based Active Learning. International Conference on Artificial Intelligence and Statistics. 2801–2809. 4 indexed citations

8.

Cortes, Corinna, et al.. (2019). Active learning with disagreement graphs. International Conference on Machine Learning. 1379–1387. 2 indexed citations

9.

Cortes, Corinna, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, & Scott Cheng‐Hsin Yang. (2018). Online learning with abstention. International Conference on Machine Learning. 1059–1067. 7 indexed citations

10.

Li, Lisha, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, & Ameet Talwalkar. (2017). Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. International Conference on Learning Representations. 40 indexed citations

11.

Cortes, Corinna, Giulia DeSalvo, Vitaly Kuznetsov, Mehryar Mohri, & Scott Cheng‐Hsin Yang. (2017). Multi-Armed Bandits with Non-Stationary Rewards. arXiv (Cornell University). 2 indexed citations

12.

Li, Lisha, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, & Ameet Talwalkar. (2017). Hyperband: a novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research. 18(1). 6765–6816. 510 indexed citations breakdown →

13.

Cortes, Corinna, Giulia DeSalvo, & Mehryar Mohri. (2016). Boosting with Abstention. Neural Information Processing Systems. 29. 1660–1668. 13 indexed citations

14.

DeSalvo, Giulia & Mehryar Mohri. (2016). Random Composite Forests. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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