8 citations · 19 across the 6 of their papers we have counts for
4 papers · 1 filter
Wanda++: Pruning Large Language Models via Regional Gradients
Yifan Yang, Kai Zhen, Bhavana Ganesh +11
Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy de…
AutoML Two-Sample Test
Jonas M. Kübler, Vincent Stimper, Simon Buchholz +2
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of m…
A Witness Two-Sample Test
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf +1
The Maximum Mean Discrepancy (MMD) has been the state-of-the-art nonparametric test for tackling the two-sample problem. Its statistic is given by the difference in expectations of…
Learning Kernel Tests Without Data Splitting
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf +1
Modern large-scale kernel-based tests such as maximum mean discrepancy (MMD) and kernelized Stein discrepancy (KSD) optimize kernel hyperparameters on a held-out sample via data sp…