Browsing by Author "Castillo, Carlos"

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  • Karimi-Haghighi, Marzieh; Castillo, Carlos; Hernández Leo, Davinia (Springer, 2022)
    In this work, we evaluate the risk of early dropout in undergraduate studies using causal inference methods, and focusing on groups of students who have a relatively higher dropout risk. We use a large dataset consisting ...
  • Solans Noguero, David (Universitat Pompeu Fabra, 2022-09-14)
    The fast-growing adoption of technologies based on Machine Learning (ML), in addition to the large scale at which they operate, makes them a potential source of systematic discrimination against disadvantaged social ...
  • Anagnostopoulos, Aris; Castillo, Carlos; Fazzone, Adriano; Leonardi, Stefano; Terzi, Evimaria (ACM Association for Computer Machinery, 2018)
    Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, traditional forms of “in-sourcing” work continue being the dominant form of employment. This ...
  • Porcaro, Lorenzo (Universitat Pompeu Fabra, 2023-02-06)
    This thesis focuses on assessing the impact that music recommendation diversity may have on listeners. In the music domain, diversity is one of the values that recommender systems should preserve, because the world music ...
  • Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane (Mary Ann Liebert, Inc, 2016)
    Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than ...
  • Hertweck, Corinna; Castillo, Carlos; Mathioudakis, Michael (Society for Learning Analytics Research, 2022)
    We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that ...
  • Kumar, Pakhee; Ofli, Ferda; Imran, Muhammad; Castillo, Carlos (ACM Association for Computer Machinery, 2020)
    This article describes a method for early detection of disaster-related damage to cultural heritage. It is based on data from social media, a timely and large-scale data source that is nevertheless quite noisy. First, we ...
  • Porcaro, Lorenzo; Castillo, Carlos; Gómez Gutiérrez, Emilia, 1975- (Ubiquity Press, 2021)
    Music Recommender Systems (Music RS) are nowadays pivotal in shaping the listening experience of people all around the world. Partly driven by the commercial application of this technology, music recommendation research ...
  • Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (ACM Association for Computer Machinery, 2022)
    Music listening in today’s digital spaces is highly characterized by the availability of huge music catalogues, accessible by people all over the world. In this scenario, recommender systems are designed to guide listeners ...
  • Proskurnia, Julia; Mavlyutov, Ruslan; Castillo, Carlos; Aberer, Karl; Cudré-Mauroux, Philippe (ACM Association for Computer Machinery, 2017)
    Automatically extracting information from social media is challenging given that social content is often noisy, ambiguous, and inconsistent. However, as many stories break on social channels first before being picked up ...
  • Miron, Marius; Tolan, Songül; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (Springer, 2020)
    In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning (ML) algorithms. We show that in our dataset, containing hundreds of cases, ML models ...
  • Shakespeare, Dougal; Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (CEUR Workshop Proceedings, 2020)
    Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users’ ...
  • Zehlike, Meike; Bonchi, Francesco; Castillo, Carlos; Hajian, Sara; Megahed, Mohamed; Baeza Yates, Ricardo (ACM Association for Computer Machinery, 2017)
    In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n>>k candidates, maximizing utility (i.e., select the “best” candidates) subject ...
  • Zehlike, Meike; Sühr, Tom; Castillo, Carlos; Kitanovski, Ivan (ACM Association for Computer Machinery, 2020)
    Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, ...
  • Pandey, Rahul; Castillo, Carlos; Purohit, Hemant (ACM Association for Computer Machinery, 2019)
    High-quality human annotations are necessary to create effective machine learning systems for social media. Low-quality human annotations indirectly contribute to the creation of inaccurate or biased learning systems. We ...
  • Vitiugin, Fedor (Universitat Pompeu Fabra, 2023-10-26)
    Social media is a valuable platform for sharing real-time perspectives and insights, particularly during dynamic events. Extracting relevant information from social media during emergencies can be challenging, especially ...
  • Porcaro, Lorenzo; Castillo, Carlos; Gómez Gutiérrez, Emilia, 1975- (International Society for Music Information Retrieval (ISMIR), 2019)
    Music recommendations are increasingly part of the listening experience of people all over the world, especially in the context of streaming services. In this scenario, recommender systems’ role is to help users in ...
  • Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (ACM Association for Computer Machinery, 2022)
    Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and ...
  • Solans, David; Biggio, Battista; Castillo, Carlos (Springer, 2020)
    Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data. While the effects ...
  • Proskurnia, Julia; Grabowicz, Przemyslaw; Kobayashi, Ryota; Castillo, Carlos; Cudré-Mauroux, Philippe; Aberer, Karl (ACM Association for Computer Machinery, 2017)
    Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of ...

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