23 citations · 36 across the 6 of their papers we have counts for
6 papers
Normative Alignment of Recommender Systems via Internal Label Shift
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions…
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow +4
We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deploym…
GeoFormer: A Multi-Polygon Segmentation Transformer
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmen…
EB-NeRD: A Large-Scale Dataset for News Recommendation
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challeng…
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender…
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
Paul Jeha, Will Grathwohl, Michael Riis Andersen +2
Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hind…