This map shows the geographic impact of Isabel Valera'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 Isabel Valera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Isabel Valera more than expected).
This network shows the impact of papers produced by Isabel Valera. 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 Isabel Valera. The network helps show where Isabel Valera may publish in the future.
Co-authorship network of co-authors of Isabel Valera
This figure shows the co-authorship network connecting the top 25 collaborators of Isabel Valera.
A scholar is included among the top collaborators of Isabel Valera 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 Isabel Valera. Isabel Valera is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karimi, Amir-Hossein, Julius von Kügelgen, Bernhard Schölkopf, & Isabel Valera. (2020). Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Cambridge University Engineering Department Publications Database. 33. 265–277.7 indexed citations
7.
Valera, Isabel, Melanie F. Pradier, María Lomelí, & Zoubin Ghahramani. (2020). General Latent Feature Models for Heterogeneous Datasets. Journal of Machine Learning Research. 21(100). 1–49.2 indexed citations
8.
Kilbertus, Niki, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, & Isabel Valera. (2020). Fair Decisions Despite Imperfect Predictions. 277–287.8 indexed citations
9.
Zafar, Muhammad Bilal, Isabel Valera, Manuel Gomez-Rodriguez, & Krishna P. Gummadi. (2019). Fairness Constraints: A Flexible Approach for Fair Classification. Journal of Machine Learning Research. 20(75). 1–42.95 indexed citations
10.
Adel, Tameem, Isabel Valera, Zoubin Ghahramani, & Adrian Weller. (2019). One-Network Adversarial Fairness. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 2412–2420.48 indexed citations
De, Abir, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, & Manuel Gomez-Rodriguez. (2016). Learning and Forecasting Opinion Dynamics in Social Networks. MPG.PuRe (Max Planck Society). 29. 397–405.35 indexed citations
15.
Valera, Isabel, Francisco J. R. Ruiz, Lennart Svensson, & Fernando Pérez‐Cruz. (2015). Infinite factorial dynamical model. Chalmers Publication Library (Chalmers University of Technology). 28. 1666–1674.10 indexed citations
16.
Valera, Isabel & Zoubin Ghahramani. (2014). General Table Completion using a Bayesian Nonparametric Model. Cambridge University Engineering Department Publications Database. 27. 981–989.3 indexed citations
Farajtabar, Mehrdad, et al.. (2014). Shaping Social Activity by Incentivizing Users.. PubMed. 27.21 indexed citations
19.
Ruiz, Francisco J. R., Isabel Valera, Carlos Blanco, & Fernando Pérez‐Cruz. (2012). Bayesian Nonparametric Modeling of Suicide Attempts. Cambridge University Engineering Department Publications Database. 25. 1853–1861.7 indexed citations
20.
Valera, Isabel. (2009). A HYBRID SS-TOA WIRELESS GEOLOCATION BASED ON PATH ATTENUATION: ROBUSTNESS INVESTIGATION UNDER IMPERFECT PATH LOSS EXPONENT.
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.