10 citations · 22 across the 18 of their papers we have counts for
18 papers
Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making
Maike Lindermayr, Mattia Cerrato, Luisa Hübner +1
While fairness has become a central concern in research on algorithmic systems, the field remains predominantly shaped by Computer Science, resulting in a strong emphasis on formal…
Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse
Mattia Cerrato, Otto Sahlgren, Xenia Heilmann
Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. Thi…
Are Algorithm Registers Transparent? Perspectives from Germany
Iman Peljto, Xenia Heilmann, Mattia Cerrato
Algorithm registers are public-facing databases that display basic information about algorithms employed in public administration. While several such registers exist across Europe…
From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
Minh Duc Bui, Xenia Heilmann, Mattia Cerrato +2
Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, ex…
Rashomon Sets and Model Multiplicity in Federated Learning
Xenia Heilmann, Luca Corbucci, Mattia Cerrato
The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision boundaries. Understanding the di…
N-Parties Private Structure and Parameter Learning for Sum-Product Networks
Xenia Heilmann, Ernst Althaus, Mattia Cerrato +3
A sum-product network (SPN) is a graphical model that allows several types of probabilistic inference to be performed efficiently. In this paper, we propose a privacy-preserving pr…
Exploring the Design Space of Fair Tree Learning Algorithms
Kiara Stempel, Mattia Cerrato, Stefan Kramer
Decision trees have been studied extensively in the context of fairness, aiming to maximize prediction performance while ensuring non-discrimination against different groups. Techn…
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Xenia Heilmann, Luca Corbucci, Mattia Cerrato +1
Federated Learning (FL) enables collaborative training while preserving privacy, yet it introduces a critical challenge: the "illusion of fairness''. A global model, usually evalua…
Neural-Guided Equation Discovery
Jannis Brugger, Mattia Cerrato, David Richter +4
Deep learning approaches are becoming increasingly attractive for equation discovery. We show the advantages and disadvantages of using neural-guided equation discovery by giving a…
Predicting NOx emissions in Biochar Production Plants using Machine Learning
Marius Köppel, Niklas Witzig, Tim Klausmann +8
The global Biochar Industry has witnessed a surge in biochar production, with a total of 350k mt/year production in 2023. With the pressing climate goals set and the potential of B…
10 Years of Fair Representations: Challenges and Opportunities
Mattia Cerrato, Marius Köppel, Philipp Wolf +1
Fair Representation Learning (FRL) is a broad set of techniques, mostly based on neural networks, that seeks to learn new representations of data in which sensitive or undesired in…
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Cedric Derstroff, Mattia Cerrato, Jannis Brugger +2
Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-er…
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
Stefan Kramer, Mattia Cerrato, Jannis Brugger +2
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches…
Invariant Representations with Stochastically Quantized Neural Networks
Mattia Cerrato, Marius Köppel, Roberto Esposito +1
Representation learning algorithms offer the opportunity to learn invariant representations of the input data with regard to nuisance factors. Many authors have leveraged such stra…
Fair Interpretable Representation Learning with Correction Vectors
Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel +3
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…
Fair Interpretable Learning via Correction Vectors
Mattia Cerrato, Marius Köppel, Alexander Segner +1
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…
Fair Group-Shared Representations with Normalizing Flows
Mattia Cerrato, Marius Köppel, Alexander Segner +1
The issue of fairness in machine learning stems from the fact that historical data often displays biases against specific groups of people which have been underprivileged in the re…
Partitioned Least Squares
Roberto Esposito, Mattia Cerrato, Marco Locatelli
In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute…