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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.DS2026

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

The paper analyzes phase transitions and provides algorithms for refuting phylogenetic constraint satisfaction problems, establishing sharp density thresholds for triplet and quart…

cs.DS2026

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…

cs.DS2026

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…

cs.LG2026

Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems

Sean Gunn, Jorio Cocola, Oliver De Candido +2

Generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals using a single fixed complexity or dimen…

cs.LG2025

Accelerating data-driven algorithm selection for combinatorial partitioning problems

Vaggos Chatziafratis, Ishani Karmarkar, Yingxi Li +1

Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a co…

cs.LG2025

The Complexity of Finding Local Optima in Contrastive Learning

Jingming Yan, Yiyuan Luo, Vaggos Chatziafratis +3

Contrastive learning is a powerful technique for discovering meaningful data representations by optimizing objectives based on , often given as a…