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cs.LG2026
A Free Lunch in LLM Compression: Revisiting Retraining after Pruning
Moritz Wagner, Christophe Roux, Max Zimmer +1
Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since global retraining is expensive at…
cs.LG2026
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
Max Zimmer, Christophe Roux, Moritz Wagner +2
The resource requirements of neural networks can be significantly reduced through pruning - the removal of seemingly less important parameters. However, for LLMs, full retraining t…
cs.LG2025
S-CFE: Simple Counterfactual Explanations
Shpresim Sadiku, Moritz Wagner, Sai Ganesh Nagarajan +1
We study the problem of finding optimal sparse, manifold-aligned counterfactual explanations for classifiers. Canonically, this can be formulated as an optimization problem with mu…