58 citations · 178 across the 20 of their papers we have counts for
22 papers
Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration
Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1
Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of their accuracy. As attention…
Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
Ervin Dervishaj, Maria Maistro, Tuukka Ruotsalo +1
Implicit feedback -- the main data source for training Recommender Systems (RSs) -- is inherently noisy and has been shown to negatively affect recommendation effectiveness. Denois…
Measuring Individual User Fairness with User Similarity and Effectiveness Disparity
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Individual user fairness is commonly understood as treating similar users similarly. In Recommender Systems (RSs), several evaluation measures exist for quantifying individual user…
The Quest for Reliable Metrics of Responsible AI
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quant…
Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +2
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relation…
Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
Jonathan Grizou, Carlos de la Torre-Ortiz, Tuukka Ruotsalo
We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived f…
Correcting Gradient-Based Circuit Localization via Interaction-Aware Backpropagation
Joakim Edin, Casper L. Christensen, Róbert Csordás +5
Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis. Mos…
As easy as PIE: understanding when pruning causes language models to disagree
Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1
Language Model (LM) pruning compresses the model by removing weights, nodes, or other parts of its architecture. Typically, pruning focuses on the resulting efficiency gains at the…
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance,…
Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study
Ervin Dervishaj, Tuukka Ruotsalo, Maria Maistro +1
Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by…
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attribution Explainability
Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen +3
Deep neural network predictions are notoriously difficult to interpret. Feature attribution methods aim to explain these predictions by identifying the contribution of each input f…
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
Joakim Edin, Maria Maistro, Lars Maaløe +3
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantl…
Modeling User Preferences via Brain-Computer Interfacing
Luis A. Leiva, V. Javier Traver, Alexandra Kawala-Sterniuk +1
Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which su…
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only…
Physiological Data: Challenges for Privacy and Ethics
Keith Davis, Tuukka Ruotsalo
Wearable devices that measure and record physiological signals are now becoming widely available to the general public with ever-increasing affordability and signal quality. The da…
Query Augmentation by Decoding Semantics from Brain Signals
Ziyi Ye, Jingtao Zhan, Qingyao Ai +4
Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved,…
Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing
Keith M. Davis, Michiel Spapé, Tuukka Ruotsalo
We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experim…
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we e…
BrainLLM: Generative Language Decoding from Brain Recordings
Ziyi Ye, Qingyao Ai, Yiqun Liu +4
Generating human language through non-invasive brain-computer interfaces (BCIs) has the potential to unlock many applications, such as serving disabled patients and improving commu…
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Joakim Edin, Alexander Junge, Jakob D. Havtorn +4
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatm…
Interactive Visual Facets to Support Fluid Exploratory Search
Chen He, Luana Micallef, Barış Serim +3
Exploratory search starts with ill-defined goals and involves browsing, learning, and formulating new targets for search. To fluidly support such dynamic search behaviours, we focu…
Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
Manuel J. A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé +4
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is…