Seminar | TransferLab — appliedAI Institute

Seminar

The TransferLab Seminar is a platform where researchers and engineers share and discuss recent advances in AI/ML, striking a balance between accessibility and mathematical depth.

We organize comprehensive talks on ML, RL, DL and statistics, emphasizing both theoretical understanding and practical implementation. These range from introductory overviews to recent developments - and are open to all interested individuals!

Each talk, lasting about 45min followed by around 15min discussion, focuses on a specific topic related to our ongoing series topics. To stay informed, follow the appliedAI Institute on LinkedIn. If you’re interested in presenting your work, contact us. Recordings of past seminars are available on YouTube.

Upcoming Talks

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Previous Talks

Shapley values for XAI: the good, the bad and the ugly - Anes Benmerzoug (appliedAI Initiative)

Jun 7, 2023

In this talk Anes will ask questions like: What is the true significance of Shapley values as feature importance measures? How can Shapley Residuals help us quantify its limits? How can we better understand global feature contributions with additive importance measures? Join us as we merge game theory with machine learning in today’s session.

Anes Benmerzoug

The debate on the accuracy-interpretability tradeoff - Miguel de Benito Delgado

Jun 1, 2023

In this talk we look into the debate on the alleged trade-off between accuracy and interpretability. We will discuss the literature on the implications of enforcing interpretability as an optimization constraint, navigating the contention of opting for interpretable models over black box models. We will explore simple and powerful models which you can use to understand your data better, while we ask ourselves whether it is worth trying to use these or not, and what new problems interpretability brings to the table.

Miguel de Benito Delgado

Introduction to Explainable AI - Faried Abu Zaid (appliedAI Institute)

May 17, 2023

Safety and reliability concerns are major obstacles for the adoption of AI in practice. In addition, European regulation will make explaining the decisions of models a requirement for so-called high-risk AI applications. Explainable AI (XAI) is an emergent field that tries to tackle AI related challenges by providing better insights into the decision-making process of machine learning models.

Faried Abu Zaid

Sampling Free Epistemic Uncertainty Through Approximated Variance Propagation - Faried Abu Zaid (appliedAI Institute)

Feb 7, 2023

Noise injection methods such as dropout are popular ways of implicitly capturing epistemic uncertainty in neural networks. Usually, noise injection is applied during training and inference. In the training phase, the networks learns to reduce the variance that is introduced by noise injection within the data distribution. Running inference several times on the same input with noise injection enabled makes it possible to estimate the remaining uncertainty, which will be mostly epistemic.

Uncertainty Quantification

Faried Abu Zaid

Uncertainty quantification with conformal prediction - Miguel de Benito Delgado

Jan 24, 2023

Conformal prediction is a method for uncertainty quantification that is based on the idea of conformal sets (CS). A CS is a set of predictions that is guaranteed to contain the true value with a certain probability. This probability is called the confidence level of the CS. The CSs are constructed such that the confidence level is independent of the data distribution and the model’s structure or uncertainty.

Uncertainty Quantification

Miguel de Benito Delgado

Preventing bias through decision-making algorithms - Carmen MazijnLisa Koutsovit-koumeri

Jan 10, 2023

Decisions made by Artificial Intelligence (AI) systems often achieve better performance compared to humans. Nevertheless, these decisions can still be unfair as proven by several studies. Therefore, research efforts are nowadays directed toward creating fair decision-making models (or algorithms). This is a challenging task due to several reasons, including, but not limited to, detecting the source of bias in the data-generating process, defining the applicable notion of fairness (outcome or treatment), finding how bias is encoded in the data (directly, indirectly or multi-dimensionally) and, making sure that the decision-making algorithms do not create, propagate or amplify existing biases.

Lisa Koutsovit-koumeri

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Our Block Seminars

Methods and issues in Explainable AI

This seminar series explores the landscape of Explainable Artificial Intelligence (XAI) beyond the content in our training on XAI, and through a comprehensive selection of papers in the field. By the end of this series, participants will …

May 17, 2023

Uncertainty quantification for neural networks

In this seminar series, we review seminal and recent papers on uncertainty quantification that didn’t make it into our training on Bayesian ML.

Oct 22, 2022