Demo Data from EDBT 2026 / reviewers in the wild / expert
Diego Garcia-Olano
dblp:166/5010
· DBLP profile ↗
4ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0003-2143-2892ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 75% Language models and text generation · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › large language model evaluation
error discovery |
0.7 | 1 | 2023 | Error Discovery By Clustering Influence Embeddings · NeurIPS 2023 |
Machine learning › Trustworthy machine learning
interpretability |
0.7 | 1 | 2023 | Error Discovery By Clustering Influence Embeddings · NeurIPS 2023 |
Machine learning › Trustworthy machine learning › interpretability
model debugging |
0.7 | 1 | 2023 | Error Discovery By Clustering Influence Embeddings · NeurIPS 2023 |
Machine learning › Trustworthy machine learning
slice discovery |
0.7 | 1 | 2023 | Error Discovery By Clustering Influence Embeddings · NeurIPS 2023 |
Methods — techniques the papers use, named apart from their topics
k-means clustering · 0.7influence functions · 0.7influence embedding · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefilling
Silvia Cappelletti, Tobia Poppi, Samuele Poppi, Diego Garcia-Olano, Marcella Cornia, Lorenzo Baraldi 0001, Rita Cucchiara |
ICPR (3) | 5 |
| 2023 | Error Discovery By Clustering Influence EmbeddingsabstractWe present a method for identifying groups of test examples---slices---on which a model under-performs, a task now known as slice discovery. We formalize coherence---a requirement that erroneous predictions, within a slice, should be wrong for the same reason---as a key property that any slice discovery method should satisfy. We then use influence functions to derive a new slice discovery method, InfEmbed, which satisfies coherence by returning slices whose examples are influenced similarly by the training data. InfEmbed is simple, and consists of applying K-Means clustering to a novel representation we deem influence embeddings. We show InfEmbed outperforms current state-of-the-art methods on 2 benchmarks, and is effective for model debugging across several case studies. Fulton Wang, Julius Adebayo, Sarah Tan, Diego Garcia-Olano, Narine Kokhlikyan |
NeurIPS | 4 |
| 2019 | Learning Dense Representations for Entity RetrievalabstractDaniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, Diego Garcia-Olano. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). 2019. Daniel Gillick, Sayali Kulkarni, Larry Lansing, Alessandro Presta, Jason Baldridge, Eugene Ie, Diego Garcia-Olano |
CoNLL | 7 |
| 2015 | Term-frequency Inverse Document Frequency for the Assessment of Similarity in Central and State Climate Change Programs: An Example for Mexico
Iván Paz Ortiz, Diego Garcia-Olano, Carlos Gay-García |
SIMULTECH | 2 |