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Ze Peng

2 accepted papers

2026

When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging

ICML 2026poster

Model merging combines multiple fine-tuned models into a single model by $\textit{adding}$ their weight updates, providing a lightweight alternative to retraining. Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over-counting shared knowledge u…

Cited by 0SourceScholar
2025

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD

ICLR 2025poster

Information-theoretic (IT) generalization bounds have been used to study the generalization of learning algorithms. These bounds are intrinsically data- and algorithm-dependent so that one can exploit the properties of data and algorithm to derive tighter bounds. However, we observe that although th…