An efficient algorithm recovers phylogenetic trees from Θ(n) noisy quartets under random classification noise, matching the information-theoretic lower bound and achieving near-optimal quartet distance.
Journal of Machine Learning Research , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Proposes Factor-Augmented SGD that runs on streaming high-dimensional data and supplies the first convergence analysis explicitly accounting for latent-factor estimation error.
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
Triplet constraints realizable in D-dimensional Euclidean space cannot be preserved above 50% accuracy by any embedding of dimension at most cD for constant c<1, with UGC-hardness preventing better polynomial-time solutions in any dimension.
citing papers explorer
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Optimal Phylogenetic Reconstruction from Sampled Quartets
An efficient algorithm recovers phylogenetic trees from Θ(n) noisy quartets under random classification noise, matching the information-theoretic lower bound and achieving near-optimal quartet distance.
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Factor Augmented High-Dimensional SGD
Proposes Factor-Augmented SGD that runs on streaming high-dimensional data and supplies the first convergence analysis explicitly accounting for latent-factor estimation error.
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Hyperbolic Latent Space Models for Network Embedding: Model Specification and Bayesian Inference
A Bayesian hyperbolic latent space model with inferable temperature parameter outperforms fixed-temperature and Euclidean models in network reconstruction by better capturing tree-like topologies.
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Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch
Triplet constraints realizable in D-dimensional Euclidean space cannot be preserved above 50% accuracy by any embedding of dimension at most cD for constant c<1, with UGC-hardness preventing better polynomial-time solutions in any dimension.