This report presents the winning solution for Task 2 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The goal of the challenge was to design and train a robust ANN-based model capable of achieving high accuracy in a binary classification task on both clean and adversarial data generated with the Random Distribution Shuffle Attack (RDSA). Our solution consists of two components: a data generation phase and a robust model training phase. In the first phase, we produced 15 million artificial training samples using a custom methodology derived from Random Distribution Shuffle Attack (RDSA). In the second phase, we introduced a robust architecture comprising (i)a Feature Embedding Block with shared weights among features of the same type and (ii)a Dense Fusion Tail responsible for the final prediction. Training this architecture on our adversarial dataset achieved a mixed accuracy score of 80\%, exceeding the second-place solution by two percentage points.

This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery at ECML-PKDD 2025. The task required designing an adversarial attack against a provided classification model that maximizes misclassification while minimizing perturbations. Our approach employs a multi-round gradient-based strategy that leverages the differentiable structure of the model, augmented with random initialization and sample-mixing techniques to enhance effectiveness. The resulting attack achieved the best results in perturbation size and fooling success rate, securing first place in the competition.

It has been over 10 years since the Netflix Prize finished, and I was not expecting to write a blog post about it at this point. However, just in the past couple of weeks I have found myself talking about it extensively both in the context of a Twitter thread and discussion, as well as the shooting of an upcoming documentary series. Given that there seems to be continued interest as well as misunderstanding around the prize and its outcome, I thought it might be worth to "set the record straight" in a dedicated post. TLDR; While I am often misquoted as having said that the Netflix Prize was not useful for Netflix, that is only true about the grand prize winning entry. Along the way, Netflix got far more than our money's worth for the famous prize.