34 citations · 136 across the 27 of their papers we have counts for
7 papers · 1 filter
Jailbreaking Open-Weight LLMs via Random Embedding Perturbations
Abhinav Sudhakar Dubey, Scott Sirri, Vaggos Chatziafratis +1
While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important concern. One key feature is the a…
Lend Me Your Eyes: Instruction-Aware Text Embeddings via Attention Relay
Yiyuan Luo, Vaggos Chatziafratis
Text embedding models trained with contrastive learning learn to follow task instructions from instruction-paired data, while instruction-tuned LLMs already know how to follow them…
Optimal VC Dimension of Contrastive Learning with Margin
Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo +1
Contrastive learning is a successful paradigm for learning -dimensional geometric representations from a collection of ``anchor--positive--negative'' triplets ,…
Strong Refutation of Ordering, Phylogenetic, and Ordinary CSPs, and New Satisfiability and Refutation Thresholds for Triplet and Quartet Reconstruction
Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo +1
We study phase transitions and algorithms for refuting CSPs arising in hierarchical clustering (as well as ranking, and ordinary CSPs). Here, variables are assigned to leaves o…
Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch
Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo
Embedding-based representations in Euclidean space are a cornerstone of modern machine learning, where a major goal is to use the \emph{smallest dimension} that fait…
Optimal Phylogenetic Reconstruction from Sampled Quartets
Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo +1
Quartet Reconstruction, the task of recovering a phylogenetic tree from smaller trees on four species called \textit{quartets}, is a well-studied problem in theoretical computer sc…
Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
Sean Gunn, Jorio Cocola, Oliver De Candido +2
Latent generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals at a single, fixed complexity, go…