Search Results for author: Carlos Castillo

Found 33 papers, 11 papers with code

Predicting Early Dropout: Calibration and Algorithmic Fairness Considerations

no code implementations16 Mar 2021 Marzieh Karimi-Haghighi, Carlos Castillo, Davinia Hernandez-Leo, Veronica Moreno Oliver

In this work, the problem of predicting dropout risk in undergraduate studies is addressed from a perspective of algorithmic fairness.

Fairness

Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics

1 code implementation28 Jan 2021 Lorenzo Porcaro, Emilia Gómez, Carlos Castillo

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 of understanding how diversity is perceived by end-users.

Information Retrieval Music Information Retrieval

A Note on the Significance Adjustment for FA*IR with Two Protected Groups

no code implementations23 Dec 2020 Meike Zehlike, Tom Sühr, Carlos Castillo

In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a low minimum proportion $p$ for the protected group.

Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods

no code implementations10 Dec 2020 Valerio Lorini, Carlos Castillo, Domenico Nappo, Francesco Dottori, Peter Salamon

Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this.

Exploring Artist Gender Bias in Music Recommendation

1 code implementation3 Sep 2020 Dougal Shakespeare, Lorenzo Porcaro, Emilia Gómez, Carlos Castillo

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' specific music preferences.

Collaborative Filtering Recommendation Systems

SciLens News Platform: A System for Real-Time Evaluation of News Articles

no code implementations27 Aug 2020 Angelika Romanou, Panayiotis Smeros, Carlos Castillo, Karl Aberer

We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles.

Towards Data-Driven Affirmative Action Policies under Uncertainty

no code implementations2 Jul 2020 Corinna Hertweck, Carlos Castillo, Michael Mathioudakis

In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs.

Poisoning Attacks on Algorithmic Fairness

1 code implementation15 Apr 2020 David Solans, Battista Biggio, Carlos Castillo

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.

Fairness

Addressing multiple metrics of group fairness in data-driven decision making

1 code implementation10 Mar 2020 Marius Miron, Songül Tolan, Emilia Gómez, Carlos Castillo

The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric.

Decision Making Fairness

Algorithms for Hiring and Outsourcing in the Online Labor Market

no code implementations16 Feb 2020 Aris Anagnostopoulos, Carlos Castillo, Adriano Fazzone, Stefano Leonardi, Evimaria Terzi

In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.

Uneven Coverage of Natural Disasters in Wikipedia: the Case of Flood

1 code implementation23 Jan 2020 Valerio Lorini, Javier Rando, Diego Saez-Trumper, Carlos Castillo

We also note how coverage of floods in countries with the lowest income, as well as countries in South America, is substantially lower than the coverage of floods in middle-income countries.

Disaster Response

EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions

1 code implementation5 Dec 2019 Oana Balalau, Carlos Castillo, Mauro Sozio

Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging.

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

no code implementations18 Aug 2019 Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, K. Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth

Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.

Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing

no code implementations16 Jul 2019 Rahul Pandey, Carlos Castillo, Hemant Purohit

High-quality human annotations are necessary to create effective machine learning systems for social media.

Active Learning

FairSearch: A Tool For Fairness in Ranked Search Results

no code implementations27 May 2019 Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski

We implement two algorithms from the fair ranking literature, namely FA*IR (Zehlike et al., 2017) and DELTR (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java.

Fairness

Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour

no code implementations7 Jun 2018 Emilia Gómez, Carlos Castillo, Vicky Charisi, Verónica Dahl, Gustavo Deco, Blagoj Delipetrev, Nicole Dewandre, Miguel Ángel González-Ballester, Fabien Gouyon, José Hernández-Orallo, Perfecto Herrera, Anders Jonsson, Ansgar Koene, Martha Larson, Ramón López de Mántaras, Bertin Martens, Marius Miron, Rubén Moreno-Bote, Nuria Oliver, Antonio Puertas Gallardo, Heike Schweitzer, Nuria Sebastian, Xavier Serra, Joan Serrà, Songül Tolan, Karina Vold

The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.

Decision Making

Reducing Disparate Exposure in Ranking: A Learning To Rank Approach

8 code implementations22 May 2018 Meike Zehlike, Carlos Castillo

Ranked search results have become the main mechanism by which we find content, products, places, and people online.

Information Retrieval Computers and Society H.3.3

The Effect of Extremist Violence on Hateful Speech Online

1 code implementation16 Apr 2018 Alexandra Olteanu, Carlos Castillo, Jeremy Boy, Kush R. Varshney

In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence.

Social and Information Networks Computers and Society

FA*IR: A Fair Top-k Ranking Algorithm

2 code implementations20 Jun 2017 Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates

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 to group fairness criteria.

Fairness

The Do's and Don'ts for CNN-based Face Verification

no code implementations21 May 2017 Ankan Bansal, Carlos Castillo, Rajeev Ranjan, Rama Chellappa

While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered.

Face Recognition Face Verification

Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

no code implementations15 Feb 2017 Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, Carlos Castillo, Rama Chellappa

Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.

Face Recognition Face Verification

Deep Convolutional Neural Network Features and the Original Image

no code implementations6 Nov 2016 Connor J. Parde, Carlos Castillo, Matthew Q. Hill, Y. Ivette Colon, Swami Sankaranarayanan, Jun-Cheng Chen, Alice J. O'Toole

The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.

Face Recognition

UMDFaces: An Annotated Face Dataset for Training Deep Networks

no code implementations4 Nov 2016 Ankan Bansal, Anirudh Nanduri, Carlos Castillo, Rajeev Ranjan, Rama Chellappa

Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets.

Face Detection Face Recognition +1

A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting

no code implementations6 Oct 2016 Muhammad Imran, Sanjay Chawla, Carlos Castillo

An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.

General Classification Outlier Detection

Biconvex Relaxation for Semidefinite Programming in Computer Vision

1 code implementation31 May 2016 Sohil Shah, Abhay Kumar, Carlos Castillo, David Jacobs, Christoph Studer, Tom Goldstein

We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.

Metric Learning

Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages

no code implementations LREC 2016 Muhammad Imran, Prasenjit Mitra, Carlos Castillo

Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.

Disaster Response Humanitarian +1

Triplet Probabilistic Embedding for Face Verification and Clustering

2 code implementations19 Apr 2016 Swami Sankaranarayanan, Azadeh Alavi, Carlos Castillo, Rama Chellappa

Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.

Face Verification

Controversy and Sentiment in Online News

no code implementations29 Sep 2014 Yelena Mejova, Amy X. Zhang, Nicholas Diakopoulos, Carlos Castillo

We find that in general, when it comes to controversial issues, the use of negative affect and biased language is prevalent, while the use of strong emotion is tempered.

Engineering Crowdsourced Stream Processing Systems

no code implementations21 Oct 2013 Muhammad Imran, Ioanna Lykourentzou, Yannick Naudet, Carlos Castillo

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream.

Says who? Automatic Text-Based Content Analysis of Television News

no code implementations18 Jul 2013 Carlos Castillo, Gianmarco De Francisci Morales, Marcelo Mendoza, Nasir Khan

We perform an automatic analysis of television news programs, based on the closed captions that accompany them.

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