Aldo Pacchiano

550 total citations
19 papers, 66 citations indexed

About

Aldo Pacchiano is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, Aldo Pacchiano has authored 19 papers receiving a total of 66 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Management Science and Operations Research and 4 papers in Computational Theory and Mathematics. Recurrent topics in Aldo Pacchiano's work include Advanced Bandit Algorithms Research (6 papers), Reinforcement Learning in Robotics (5 papers) and Machine Learning and Algorithms (4 papers). Aldo Pacchiano is often cited by papers focused on Advanced Bandit Algorithms Research (6 papers), Reinforcement Learning in Robotics (5 papers) and Machine Learning and Algorithms (4 papers). Aldo Pacchiano collaborates with scholars based in United States, United Kingdom and France. Aldo Pacchiano's co-authors include Heinrich Jiang, Krzysztof Choromański, Silvia Chiappa, Tom Stepleton, John Aslanides, Jack Parker-Holder, Peter L. Bartlett, Stephen Roberts, Yuxiang Yang and Wenbo Gao and has published in prestigious journals such as CaltechAUTHORS (California Institute of Technology), arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Aldo Pacchiano

18 papers receiving 63 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aldo Pacchiano United States 4 53 10 10 10 6 19 66
Sylvain Lamprier France 6 62 1.2× 15 1.5× 18 1.8× 4 0.4× 15 80
Serena Villata France 4 45 0.8× 3 0.3× 5 0.5× 5 0.5× 2 0.3× 8 61
Sungsoo Ahn South Korea 2 60 1.1× 29 2.9× 7 0.7× 2 0.2× 3 0.5× 4 74
Jiechuan Jiang China 3 47 0.9× 6 0.6× 2 0.2× 4 0.4× 6 1.0× 5 54
Nishant Subramani United States 5 40 0.8× 16 1.6× 8 0.8× 3 0.3× 6 67
Edward Lockhart United Kingdom 3 44 0.8× 8 0.8× 2 0.2× 2 0.2× 4 0.7× 4 57
Aurélie Névéol France 7 110 2.1× 4 0.4× 3 0.3× 4 0.4× 2 0.3× 14 134
Pouya Pezeshkpour United States 5 83 1.6× 8 0.8× 2 0.2× 5 0.5× 11 99
Rajiv Mathews United States 7 90 1.7× 6 0.6× 6 0.6× 2 0.2× 1 0.2× 13 103
Rahul Kidambi United States 5 31 0.6× 8 0.8× 4 0.4× 4 0.7× 12 40

Countries citing papers authored by Aldo Pacchiano

Since Specialization
Citations

This map shows the geographic impact of Aldo Pacchiano'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 Aldo Pacchiano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aldo Pacchiano more than expected).

Fields of papers citing papers by Aldo Pacchiano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aldo Pacchiano. 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 Aldo Pacchiano. The network helps show where Aldo Pacchiano may publish in the future.

Co-authorship network of co-authors of Aldo Pacchiano

This figure shows the co-authorship network connecting the top 25 collaborators of Aldo Pacchiano. A scholar is included among the top collaborators of Aldo Pacchiano 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 Aldo Pacchiano. Aldo Pacchiano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Parker-Holder, Jack, et al.. (2021). Deep Reinforcement Learning with Dynamic Optimism.. arXiv (Cornell University). 2 indexed citations
2.
Pacchiano, Aldo, Mohammad Ghavamzadeh, Peter L. Bartlett, & Heinrich Jiang. (2021). Stochastic Bandits with Linear Constraints. International Conference on Artificial Intelligence and Statistics. 2827–2835. 2 indexed citations
3.
Pacchiano, Aldo, et al.. (2021). Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. arXiv (Cornell University). 7412–7422. 1 indexed citations
4.
Cutkosky, Ashok, Christoph Dann, Abhimanyu Das, et al.. (2021). Dynamic Balancing for Model Selection in Bandits and RL. 2276–2285. 1 indexed citations
5.
Song, Xingyou, Wenbo Gao, Yuxiang Yang, et al.. (2020). ES-MAML: Simple Hessian-Free Meta Learning. International Conference on Learning Representations. 12 indexed citations
6.
Ball, Philip, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromański, & Stephen Roberts. (2020). Ready Policy One: World Building Through Active Learning. International Conference on Machine Learning. 1. 591–601. 9 indexed citations
7.
Parker-Holder, Jack, Aldo Pacchiano, Krzysztof Choromański, & Stephen Roberts. (2020). Effective Diversity in Population Based Reinforcement Learning. Neural Information Processing Systems. 33. 18050–18062. 2 indexed citations
8.
Pacchiano, Aldo, et al.. (2020). On Thompson Sampling with Langevin Algorithms. CaltechAUTHORS (California Institute of Technology). 1. 1 indexed citations
9.
Pacchiano, Aldo, et al.. (2020). On Approximate Thompson Sampling with Langevin Algorithms.. International Conference on Machine Learning. 6797–6807. 2 indexed citations
10.
Chiappa, Silvia, et al.. (2020). A General Approach to Fairness with Optimal Transport. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 3633–3640. 19 indexed citations
11.
Choromański, Krzysztof, Aldo Pacchiano, Jeffrey Pennington, & Yunhao Tang. (2019). KAMA-NNs: low-dimensional rotation based neural networks. International Conference on Artificial Intelligence and Statistics. 236–245. 1 indexed citations
12.
Pacchiano, Aldo, et al.. (2019). Wasserstein Fair Classification. Uncertainty in Artificial Intelligence. 862–872. 5 indexed citations
13.
Chatterji, Niladri S., Aldo Pacchiano, & Peter L. Bartlett. (2019). Online learning with kernel losses. International Conference on Machine Learning. 971–980. 2 indexed citations
14.
Choromański, Krzysztof, Aldo Pacchiano, Jack Parker-Holder, et al.. (2019). When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. arXiv (Cornell University). 1 indexed citations
15.
Pacchiano, Aldo & Yoram Bachrach. (2019). Computing Stable Solutions in Threshold Network Flow Games With Bounded Treewidth. Adaptive Agents and Multi-Agents Systems. 2153–2155. 1 indexed citations
16.
Choromański, Krzysztof, Aldo Pacchiano, Jack Parker-Holder, & Yunhao Tang. (2019). Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces..
17.
Rowland, Mark, Krzysztof Choromański, Aldo Pacchiano, et al.. (2018). Geometrically Coupled Monte Carlo Sampling. Cambridge University Engineering Department Publications Database. 31. 195–205. 3 indexed citations
18.
Bhatia, Kush, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, & Michael I. Jordan. (2018). Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. arXiv (Cornell University). 31. 7016–7025. 1 indexed citations
19.
Rowland, Mark, Aldo Pacchiano, & Adrian Weller. (2017). Conditions beyond treewidth for tightness of higher-order LP relaxations. International Conference on Artificial Intelligence and Statistics. 10–18. 1 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Sylvain Lamprier Breakdown of academic impact, for papers by Serena Villata Breakdown of academic impact, for papers by Sungsoo Ahn Breakdown of academic impact, for papers by Jiechuan Jiang Breakdown of academic impact, for papers by Nishant Subramani Breakdown of academic impact, for papers by Edward Lockhart Breakdown of academic impact, for papers by Aurélie Névéol Breakdown of academic impact, for papers by Pouya Pezeshkpour Breakdown of academic impact, for papers by Rajiv Mathews Breakdown of academic impact, for papers by Rahul Kidambi
Rankless by CCL
2026