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Guy Smorodinsky

2 accepted papers

2026

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks

ICLR 2026poster

The memorization of training data by neural networks raises pressing concerns for privacy and security. Recent work has shown that, under certain conditions, portions of the training set can be reconstructed directly from model parameters. Some of these methods exploit implicit bias toward margin ma…

Cited by 0SourceScholar
2025

SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training

NeurIPS 2025poster

Low-rank gradient-based optimization methods have significantly improved memory efficiency during the training of large language models (LLMs), enabling operations within constrained hardware without sacrificing performance. However, these methods primarily emphasize memory savings, often overlookin…

Cited by 0SourceScholar