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FairGBM: Gradient Boosting with Fairness Constraints

Tabular data is prevalent in many high-stakes domains, from financial services to public policy. In these settings, Gradient Boosted Machines (GBM) are still the state-of-the-art. However, existing in-training fairness interventions are either incompatible with GBMs, or incur significant performance losses … Read More

Dense Template Retrieval for Customer Support

Templated answers are used extensively in customer support scenarios, providing an efficient way to cover a plethora of topics, with an easily maintainable collection of templates. However, the number of templates is often too high for an agent to manually … Read More

ML-Inception: understanding where and why models work (and don’t work)

A subgroup discovery-based method has recently been proposed to understand the behavior of models in the (original) feature space. The subgroups identified represent areas of feature space where the model obtains better or worse predictive performance than on average. For … Read More

Machine Learning for Motion Planning of Autonomous Vehicles — Interaction-Aware Motion Planning in Crowded Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging as the intentions of the surrounding agents are not directly observable. Moreover, interaction is crucial to enable safe and efficient navigation in crowded scenarios. Local trajectory optimization methods, … Read More

On the Calibration of Generative Question-Answering models: State-of-the-art and Challenges

Nowadays, generative question-answering models (e.g., UnifiedQA) achieve state-of-the-art performance in various datasets. Despite their remarkable performance, these models still produce wrong answers with high confidence scores. The responsible use of such systems in high-risk applications, like healthcare, requires some guarantees … Read More

Explainable Artificial Intelligence and its role in supporting medical diagnosis

The use of deep learning algorithms in the clinical context is hindered by their lack of interpretability. One way of increasing the acceptance of such complex algorithms is by providing explanations of the decisions through the presentation of similar examples. … Read More

The perspective of the language in multimodal conversational AI for high-end fashion marketplaces

Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around … Read More

Equivariant neural networks for recovery of Hadamard matrices

Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around … Read More

Simplifying Multilingual News Clustering Through Projection From a Shared Space

The task of organizing and clustering multilingual news articles for media monitoring is essential to follow news stories in real time. Most approaches to this task focus on high-resource languages, with low-resource languages being disregarded. With that in mind, we … Read More

From Captions to Natural Language Explanations

The growing importance of the Explainable Artificial Intelligence (XAI) field has resulted in the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not enough since different end-users have … Read More