Multi-label learning is one of the hot problems in the field of machine learning. The deep neural networks used to solve it could be quite complex and have a huge capacity. This enormous capacity, however, could also be a negative, as they tend to eventually overfit the undesirable features of the data. One such feature presented in the real-world datasets is imperfect labels. A particularly common type of label imperfection is called weak labels. This corruption is characterized not only by the presence of all relevant labels but also by the addition of some irrelevant ones. In this paper, a novel method, Co-ASL, is introduced to deal with the label noise in multi-label datasets. It combines the state-of-the-art approach for multi-label learning, ASL, with the famous robust training strategy, Co-teaching. The performance of the method is then evaluated on noisy versions of MS-COCO to show the lack of overfitting and the performance improvement over the non-robust multi-label ASL.

When addressing combinatorial optimization problems, the focus is predominantly on their computational complexity, and it is often forgotten to look at the bigger picture. As a result, it is common to miss critical details which could play a major role in the overall process. One such detail is the presence of uncertainty in the real world. A naive approach might directly predict values for the uncertain parameters, without taking into account that the ultimate goal is to obtain sound decisions. Consequently, in many cases, the resulting solutions are suboptimal. This challenge is precisely the premise behind Decision-Focused Learning (DFL) framework, which is a core of this work. This study pioneers the application of the DFL framework to scheduling problems with uncertain processing times, utilizing contextual features to predict these uncertainties. By employing the promising Score Function Gradient Estimation method, the research tackles the issue of non-differentiable regret loss functions in DFL. Key contributions include the development of techniques to enhance the performance of the Score Function, an in-depth analysis of DFL's applicability to complex scheduling scenarios, and a detailed evaluation of its strengths and weaknesses. This work not only demonstrates the potential of DFL in this new context but also lays the groundwork for future research and improvements in handling uncertainty in combinatorial optimization.