Giulia DeSalvo
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 10
- Machine Learning and Data Classification 7
- Algorithms and Data Compression 4
- Reinforcement Learning in Robotics 2
- Imbalanced Data Classification Techniques 1
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- Advanced Bandit Algorithms Research 6
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- Advanced Multi-Objective Optimization Algorithms 2
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- Optimization and Search Problems 3
- Co-authors
- Afshin RostamizadehKevin JamiesonAmeet TalwalkarLisha LiMehryar MohriCorinna CortesClaudio GentileScott Cheng‐Hsin Yang
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionManagement Science and Operations Research
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Giulia DeSalvo
12 papers receiving 565 citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 306
- Computer Vision and Pattern Recognition 114
- Management Science and Operations Research 46
- Computational Theory and Mathematics 44
- Signal Processing 28
Countries citing papers authored by Giulia DeSalvo
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
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
The 19 scholars most cited alongside Giulia DeSalvo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 0 | |
| 2 | 2023 | 5 | |
| 3 | Online Active Learning with Surrogate Loss Functions | 2021 | 1 |
| 4 | Online Learning with Dependent Stochastic Feedback Graphs | 2020 | 2 |
| 5 | Adaptive Region-Based Active Learning | 2020 | 2 |
| 6 | Online Learning with Sleeping Experts and Feedback Graphs | 2019 | 5 |
| 7 | Region-Based Active Learning | 2019 | 4 |
| 8 | Active learning with disagreement graphs | 2019 | 2 |
| 9 | Online learning with abstention | 2018 | 7 |
| 10 | Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization | 2017 | 40 |
| 11 | Multi-Armed Bandits with Non-Stationary Rewards | 2017 | 2 |
| 12 | Hyperband: a novel bandit-based approach to hyperparameter optimizationbreakdown → | 2017 | 510 |
| 13 | Boosting with Abstention | 2016 | 13 |
| 14 | 2016 | 5 |
About Giulia DeSalvo
Giulia DeSalvo is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Networks and Communications, having authored 14 papers that have together received 598 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (7 papers), Advanced Bandit Algorithms Research (6 papers), Algorithms and Data Compression (4 papers), Optimization and Search Problems (3 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Reinforcement Learning in Robotics (2 papers) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (306 citations), Computer Vision and Pattern Recognition (114 citations) and Management Science and Operations Research (46 citations). Giulia DeSalvo has collaborated with scholars based in United States, Switzerland and China. Frequent 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.
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.