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First, we explore the unique characteristics of few-shot learning to ensemble multiple few-shot (FS) models by creating three alternative fusion channels. Second, we introduce the concept of focal error diversity to learn the most efficient ensemble teaming strategy, rather than assuming that an ensemble of a larger number of base models will outperform those sub-ensembles of smaller size. We develop a focal diversity ensemble pruning method to effectively prune out the candidate ensembles with low ensemble error diversity and recommend top-\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( K \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            FS ensembles with the highest focal error diversity. Finally, we capture the complex non-linear patterns of ensemble few-shot predictions by designing the learn-to-combine algorithm, which can learn the diverse weight assignments for robust ensemble fusion over different member models. Extensive experiments on representative few-shot benchmarks show that the top-K ensembles recommended by FusionShot can outperform the representative state-of-the-art (SOTA)  few-shot models on novel tasks (different distributions and unknown at training) and can prevail over existing few-shot learners in both cross-domain settings and adversarial settings. For reproducibility purposes, FusionShot trained models, results, and code are made available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/sftekin\/fusionshot\">https:\/\/github.com\/sftekin\/fusionshot<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3746457","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T10:00:58Z","timestamp":1751882458000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8662-3609","authenticated-orcid":false,"given":"Selim Furkan","family":"Tekin","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0173-7544","authenticated-orcid":false,"given":"Fatih","family":"Ilhan","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4557-1865","authenticated-orcid":false,"given":"Tiansheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-6991","authenticated-orcid":false,"given":"Sihao","family":"Hu","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0977-696X","authenticated-orcid":false,"given":"Margaret","family":"Loper","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4138-3082","authenticated-orcid":false,"given":"Ling","family":"Liu","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8070179"},{"key":"e_1_3_2_4_2","first-page":"1153","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Bronskill John","year":"2020","unstructured":"John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard Turner. 2020. 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