Article-Journal | Dimitris Sacharidis

CAPS: Cost-Aware ML Pipeline Selection

Selecting an effective machine learning pipeline requires searching through many alternatives that differ in algorithms, hyperparameters, and data preparation steps. This complexity has led to the development of Automated Machine Learning (AutoML), which systematically explores pipeline configurations with minimal human input. However, AutoML is computationally expensive, often evaluating hundreds or thousands of pipelines per dataset, even though only a small fraction are ultimately selected for deployment.

Antonios Kontaxakis, Dimitris Sacharidis, Alberto Abelló, Sergi Nadal, Alkis Simitsis

Source Attribution in Retrieval-Augmented Generation

Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko, Gabriele Fugagnoli, Dimitris Sacharidis, Katja Hose, Tomer Sagi

AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations

Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi, Katja Hose

Fairness in AI: challenges in bridging the gap between algorithms and law

Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras, Dimitris Sacharidis, Jakub Marecek, German M. Matilla, Ioannis Z. Emiris

FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups

Giorgos Giannopoulos, Dimitris Sacharidis, Nikolaos Theologitis, Loukas Kavouras, Ioannis Z. Emiris

GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

Ioannis Z. Emiris, Dimitris Fotakis, Giorgos Giannopoulos, Dimitrios Gunopulos, Loukas Kavouras, Kleopatra Markou, Eleni Psaroudaki, Dimitrios Rontogiannis, Dimitris Sacharidis, Nikolaos Theologitis, Dimitrios Tomaras, Konstantinos Tsopelas

Mobility Data Science: Perspectives and Challenges

Mohamed F. Mokbel, Mahmoud Attia Sakr, Li Xiong, Andreas Züfle, Jussara M. Almeida, Taylor Anderson, Walid G. Aref, Gennady L. Andrienko, Natalia v. Andrienko, Yang Cao, Sanjay Chawla, Reynold Cheng, Panos K. Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, Dimitrios Gunopulos, Christian S. Jensen, Joon-Seok Kim, Kyoung-Sook Kim, Peer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario A. Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Flora D. Salim, Mohamed Sarwat, Maxime Schoemans, Cyrus Shahabi, Bettina Speckmann, Egemen Tanin, Xu Teng, Yannis Theodoridis, Kristian Torp, Goce Trajcevski, Marc J. Van Kreveld, Carola Wenk, Martin Werner, Raymond Chi-Wing Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang, Esteban Zimányi

The Susceptibility of Example-Based Explainability Methods to Class Outliers

Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi, Katja Hose

And synopses for all: A synopses data engine for extreme scale analytics-as-a-service

When analyzing big data, it is often necessary to work with data synopses, approximate summaries of the data that come with guarantees. We propose a novel synopsis-as-a-service paradigm and design a Synopses Data Engine as a Service (SDEaaS) system, built on top of Apache Flink, that combines the virtues of parallel processing and stream summarization towards delivering interactive analytics at extreme scale.

Antonios Kontaxakis, Nikos Giatrakos, Dimitris Sacharidis, Antonios Deligiannakis

Auditing for Spatial Fairness

Dimitris Sacharidis, Giorgos Giannopoulos, George Papastefanatos, Kostas Stefanidis