• Inverse reinforcement learning with linearly-solvable MDPs for multiple reward functions

  Deb, Ahana (2023-10-09)

  A subclass of Markov Decision Processes (MDPs), the Linearly solvable Markov Decision Processes (LMDPs), which have discrete state space and continuous control space, allow for a significant simplification of the inverse ...
• Predictive gut microbiome analysis for health assessment

  Gil Sorribes, Manel (2023-10-04)

  Personalized medicine is a rapidly evolving field to which many resources have been devoted recently. It represents a paradigm shift from a one-size-fits-all approach to healthcare, focusing instead on tailoring treatments ...
• Inverse optimal control for modeling virus mutations in SARS-CoV-2

  Meijer, Ilse (2022)

  Inverse Optimal Control (IOC) deals with the problem of recovering an unknown cost function in a Markov decision process from expert demonstrations acting optimally. In this thesis we apply IOC to SARS-CoV-2 data. For ...
• Extraction and categorization of Japanese lexical collocations with graph-aware transformers

  Nisho, Kosuke James (2022)

  Lexical collocations may be identified and categorized in context, which is helpful for language acquisition, dictionary creation, and many other downstream NLP tasks. However, the automatic collocation extraction and ...
• Taxonomic classification of metagenomic reads using machine learning models

  Alcobé Garcia, Eduard (2022)

  Microorganisms such as bacteria can be hard to identify correctly. Most current classification techniques are based on well conserved genes, for instance the 16S ribosomal RNA (16S rRNA). Nevertheless, achieving a ...
• Task and motion planning: scaling up

  Dalmau Moreno, Magí (2022)

  This master thesis contributes to provide planning capabilities to embodied agents so that they are able to decide what sequence of actions should be taken to solve a given complex problem, involving coupled reasoning ...
• A novel message passing approach to spatial air quality prediction in urban areas

  Calo Oliveira, Sergio (2022)

  Air pollution in our cities is a very significant cause of death and worsening of the quality of life in current times. Knowing in depth this phenomenon and its details and building tools that help us to mitigate its ...
• Linguistic emergence in deep neural networks

  Locatelli Metz, Davide (2022)

  Recent work in Emergent Communication has shown that deep neural networks can develop successful strategies of referential communication. Arguably, the extent to which they can do so depends on the properties of the ...
• Multilingual lexical simplification

  Pimienta Castillo, Jorge S. (2021-09)

  This report describes, implement, and evaluate one strategy for text simplification, namely, Lexical Simplification, that aims to reduce the complexity of some words in a sentence. This process is done in two main steps, ...
• Thompson sampling for prediction with expert advice

  Sánchez Pérez de Amézaga, Claudio (2021-09)

  We study Thompson Sampling for prediction with expert advice. With Follow the leader, and Follow the perturbed leader strategies, we present relevant results in order to proceed with Thompson Sampling Algorithm. Using a ...
• Learning non-linear payoff transformations in multi-agent systems

  Fraxanet, Emma (2021-09)

  The use of Deep Reinforcement Learning methodologies has been successful in recent years in cooperative multi-agent systems. However, this success has been mostly empirical and there is a lack of theoretical understanding ...
• Selecting features of breathing & stability data to predict stress in students

  Fischer, Johannes Simon (2021-07)

  Repeated or long-time exposure to psychological stress is a risk factor for mental and physical health. Especially students are exposed to stress and lacking stress management skills make them particularly vulnerable to ...
• Predicting the use of car parks in the province of Barcelona

  Moreno Esteban, David (2021-07)

  Designing and generating wiser policies for urban systems and infrastructures is a challenge of paramount importance. Today, the cities that present the most successful transport strategies are prioritising the movement ...
• Modelling the use of car parks in the province of Barcelona

  Ferrer Sánchez, Josep (2021-07)

  Designing and generating wiser policies for urban systems and infrastructures is a challenge of paramount importance. Today, the cities that present the most successful transport strategies are prioritising the movement ...
• The bias effect of news media sources on social media users

  Kavas, Hamit (2020-09)

  Bias in media news is such an interesting topic that it increases its popularity day by day. Classification of news sources according to their political tendency is a very well studied subject. However, we have not ...
• Correlation of speech/non-speech events with photo-plethysmographic (PPG) signal

  Cámbara Ruiz, Guillermo (2019-06)

  The use of photoplethysmogram signal (PPG) for heart monitoring is commonly found nowadays in smartphones and wrist wearables. Besides heart rate or sleep monitoring common usage, it has been proved that information from ...
• Predicting multi-resistance of bacteria in an Intensive Care Unit

  Hernàndez Carnerero, Àlvar (2020-07)

  This study considers the prediction of “multi-drug” resistance (MDR) of Pseudomonas aeruginosa bacterium caused by nosocomial infections in the Intensive Care Unit (ICU). An ensemble of binary classifiers implemented ...
• Generation of Deepfakes using normalizing flows

  Valenzuela Ramírez, Andrea (2020-07)

  Deepfakes are flooding the internet network with fake synthetic media that cannot be easily distinguished by the human eye. This content can be classified into face synthesis, face swap and facial attribute manipulation. ...
• Criteria for algorithmic fairness metric selection under different supervised classification scenarios

  Breger, Clothilde (2020-09)

  The research community, (supra-)national institutions, and regular users have noticed that Artificial Intelligence and Machine Learning algorithms can amplify existing inequity between groups. One way to limit this is ...
• Neural Tree-to-Tree Transduction

  Morales, Cristian (2020-09)

  Graph-structured data is ubiquitous in the field of Natural Language Processing. For instance, directed acyclic graphs are used in semantic and syntactic dependency representations. Thus, several applications in NLP use ...

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