Popularity Bias Alignment Estimates
arXiv:2511.19999v1
Originality Synthesis-oriented
AI Analysis
This work addresses theoretical aspects of popularity bias in machine learning, but it appears incremental as it builds directly on prior results.
The paper extends the Popularity Bias Memorization theorem to arbitrary degree distributions and provides both upper and lower bounds for alignment with top-k singular hyperspace.
We are extending Popularity Bias Memorization theorem from arXiv:archive/2404.12008 in several directions. We extend it to arbitrary degree distributions and also prove both upper and lower estimates for the alignment with top-k singular hyperspace.