4 citations · 5 across the 8 of their papers we have counts for
8 papers · 1 filter
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller
Sagi Habani, Yehonathan Refael, Ofir Lindenbaum
Releasing a neural network checkpoint can leak information about its training data, and existing reconstruction results rely on gradient-descent training. We ask whether the choice…
Learning Permutation from Structure Without Supervision
Ran Eisenberg, Ofir Lindenbaum
Many learning problems require uncovering a hidden ordering that reveals structure in unordered data, such as monotonicity in sorting or spatial continuity in jigsaw reconstruction…
Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity
Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Fully finetuning foundation language models (LMs) with billions of parameters is often impractical due to high computational costs, memory requirements, and the risk of overfitting…
Unsupervised Feature Selection Through Group Discovery
Shira Lifshitz, Ofir Lindenbaum, Gal Mishne +2
Unsupervised feature selection (FS) is essential for high-dimensional learning tasks where labels are not available. It helps reduce noise, improve generalization, and enhance inte…
No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
Yehonatan Refael, Guy Smorodinsky, Ofir Lindenbaum +1
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 traini…
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
Yehonathan Refael, Guy Smorodinsky, Tom Tirer +1
Low-rank gradient-based optimization methods have significantly improved memory efficiency during the training of large language models (LLMs), enabling operations within constrain…
Conditional Deep Canonical Time Warping
Afek Steinberg, Ran Eisenberg, Ofir Lindenbaum
Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Mi…
Contextual Feature Selection with Conditional Stochastic Gates
Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz +4
Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal s…