Master projects
Here you can find all our available master projects.
Open Projects (24)
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Reinforcement Learning for Efficient Causal Discovery
Understanding causal relationships within data is essential across fields such as healthcare, economics, and social sciences, where knowing "what causes what" guides decision-making and policy. Causal discovery, the process of identifying these relationships and structuring them in causal graphs, remains challenging, especially in complex, …
Devendra Dhami
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Maryam Tavakol
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Causal Discovery for Offline Model-Based Reinforcement Learning
Reinforcement Learning (RL) has proven effective in a variety of complex decision-making tasks. However, traditional RL requires extensive online interactions, making it costly and, in some domains, impractical due to constraints on safety, time, or resource availability. Offline RL, which relies solely on pre-collected …
Maryam Tavakol
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Devendra Dhami
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Context-Aware Model-Based Offline Reinforcement Learning
Offline Reinforcement Learning (RL) deals with the problems where simulation or online interaction is impractical, costly, and/or dangerous, allowing to automate a wide range of applications from healthcare and education to finance and robotics. However, learning new policies from offline data suffers from distributional …
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Maryam Tavakol
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Pattern discovery to improve overlay control loop by using Bayesian inference tooling
This assignment aims to detect and quantify persistent overlay improvements by investigating a larger data set systematically.It will provide you with insights into the overlay performance of ASML lithography machines. You will also learn how ASML maintains machine performance via drift control strategy. As …
Erik Quaeghebeur
More infoSRSejong Park, Hamideh Rostami -
Examples of missing not at random (MNAR) data that are difficult for classifiers
It often occurs in datasets that there is missing data. A good introduction can be found here: https://stefvanbuuren.name/fimd/.This missingness might be "completely at random" (MCAR). This occurs when the probability of being missing is the same for all cases. An example of MCAR data …
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Arthur van Camp
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Implementation of inference algorithms for the imprecise Plackett–Luce model
The Plackett–Luce model is a popular parametric probabilistic model to define distributions between rankings of objects, modelling for instance observed preferences of users or ranked performances of algorithms. Since such observations may be scarce (users may provide partial preferences, or not all algorithms are …
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Arthur van Camp
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Breeding Program Optimization via Offline Reinforcement Learning
Crop breeding programs aim to develop new cultivars with desirable traits through controlled mating within a population, enhancing agricultural productivity while reducing land use, greenhouse gas emissions, and water consumption. However, these programs face challenges like long turnover times, complex decision-making, long-term goals, and climate …
Maryam Tavakol
More infoIAIoannis Athanasiadis -
Uncertainty Estimation in Model-Based Offline Reinforcement Learning using Random Networks
Offline Reinforcement Learning (RL) deals with the problems where simulation or online interaction is impractical, costly, and/or dangerous, allowing to automate a wide range of applications from healthcare and education to finance and robotics. However, learning new policies from offline data suffers from distributional …
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Maryam Tavakol
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Long-term Fairness with Offline Reinforcement Learning
One of the main concerns in the recent AI research is that most data-driven approaches preserve the bias or unfairness available in the collected (offline) data in the resulting models, which could lead to harmful social and ethical effects in the society. Fairness-aware machine learning has …
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Maryam Tavakol
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Implementing inference algorithms for choice functions
In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …
Arthur van Camp
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Cassio de Campos
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Generative Random Forests: The Next Level
The work on generative random forests has started, but there is a long way to make them practical. This project aims at studying the drawbacks of such models and improving them with better ensemble ideas, gradient boosting, and/or other techniques already employed with decision …
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Cassio de Campos
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Probabilistic Circuits versus Bayesian networks
This project aims to compare two different types of generative models: tractable probabilistic circuits and Bayesian networks of bounded tree-width, and potentially have tools to translate between them (when possible). Probabilistic circuits have been recently applied to a number of tasks, but there is …
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Cassio de Campos
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Hybrid Bayesian networks
This internal project aims at developing and testing (for example in classification tasks) a generative model based on probabilistic graphical models for domains with continuous and categorical variables. We want to learn both the graph structure and parameters of such models while constraining their …
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Cassio de Campos
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Synthetic data generation for causal learning
An arguably major difficulty for improving causal inferences is the lack of availability of data. While observational data are abundant, interventional data are not. This internal project aims at creating software tools to generate data that can be useful for testing causal learning approaches. …
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Cassio de Campos
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Scalable Implementation of Probabilistic Circuits
This internal project aims at designing and development a usable software package for learning and reasoning with probabilistic circuits. Probabilistic circuits are models which can represent complicated mixture models and their computation circuit can be wide and deep. Because they have a structure which …
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Cassio de Campos
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Algorithms for forward irrelevance with choice functions
In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …
Arthur van Camp
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Cassio de Campos
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Concepts of independence for choice functions
In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …
Arthur van Camp
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Cassio de Campos
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Local inference algorithms for choice functions
In recent years, imprecise-probabilistic choice functions have gained growing interest, primarily from a theoretical point of view. These versatile and expressive uncertainty models have demonstrated their capacity to represent decision-making scenarios that extend beyond simple pairwise comparisons of options, accommodating situations of indecision as …
Arthur van Camp
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Cassio de Campos
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Interventional Whittle Sum-Product Networks
Whittle sum-product networks [1] model the joint distribution of multivariate time series by leveraging the Whittle approximation, casting the likelihood in the frequency domain, and place a complex-valued sum-product network over the frequencies. The conditional independence relations among the time series can then be …
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Devendra Dhami
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Dynamic Knowledge Graph Embeddings
Knowledge graph embeddings are an important area of research inside machine learning and has become a necessity due to the importance of reasoning about objects, their attributes and relations in large graphs. There have been several approaches that have been explored and can be …
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Devendra Dhami
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Efficient Unbiased Training of Large-scale Distributed RL
It is widely known that training deep neural networks on huge datasets improves learning. However, huge datasets and deep neural networks can no longer be trained on a single machine. One common solution is to train using distributed systems. In addition to traditional data-centers, …
Maryam Tavakol
More infoARAli Ramezani-Kebrya -
Computational Complexity of Probabilistic Circuits
This internal project aims at studying and devising new bounds for the computational complexity of inferences in probabilistic circuits and their robust/credal counterpart, including approximation results and fixed-parameter tractability. It requires mathematical interest and good knowledge of theory of computation. This is a theoretical …
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Cassio de Campos
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Learning Bayesian Networks in a Single Step
This internal project aims at implementing a new approach to learning the structure and parameters of Bayesian networks. It is mostly an implementation project, as the novel ideas are already established (but never published, so the approach is novel). It requires high expertise in …
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Cassio de Campos
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Battle of the credal networks: strong independence or forward irrelevance?
Bayesian networks are a popular model in AI. Credal networks are a robust version of Bayesian networks created by replacing the conditional probability mass functions describing the nodes by conditional credal sets (sets of probability mass functions). Next to their nodes, Bayesian networks are …
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Erik Quaeghebeur
Assigned Projects
No currently assigned Projects.
Finished Projects (21)
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Model Transfer for Offline Reinforcement LearningNov 2024
Offline Reinforcement Learning (RL) deals with the problems where simulation or online interaction is impractical, costly, and/or dangerous, allowing to automate a wide range of applications from healthcare and education to finance and robotics. However, learning new policies from offline data suffers from distributional shifts …
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Maryam Tavakol
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On Neural Cellular AutomataJul 2024
The design of collective intelligence, i.e. the ability of a group of simple agents to collectively cooperate towards a unifying goal, is a growing area of machine learning research aimed at solving complex tasks through emergent computation [1, 2]. The interest in these techniques …
Erik Quaeghebeur
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Gennaro Gala
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Efficient surrogates for the Ainslie wind turbine wake modelAug 2023
In wind farms, one source of reduction in power generation by the turbines is the reduction of wind speed in the wake downstream of each turbine's rotor. Namely, a turbine downstream in the wind direction of another will effectively experience wind with a reduced …
Erik Quaeghebeur
More infoLBLaurens Bliek (Information Systems, IE&IS) -
Identifying robust instances in classificationJul 2023
In a classification task, some instances are classified more robustly than others. Namely, even with a large modification of the training set, these instances (in the test set) will be assigned to the same class. Other instances are non-robust in the sense that a …
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Erik Quaeghebeur