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Found 6 papers, 1 papers with code
A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
In particular, we derive optimal detection rules for these watermarks under our framework.
Coreset selection can accelerate quantum machine learning models with provable generalization
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges.
CARE: Large Precision Matrix Estimation for Compositional Data
High-dimensional compositional data are prevalent in many applications.
Heterogeneous Federated Learning on a Graph
In this work, we consider parameter estimation in federated learning with data distribution and communication heterogeneity, as well as limited computational capacity of local devices.
Nonasymptotic theory for two-layer neural networks: Beyond the bias-variance trade-off
Large neural networks have proved remarkably effective in modern deep learning practice, even in the overparametrized regime where the number of active parameters is large relative to the sample size.
Relating the structure of dark matter halos to their assembly and environment
Using decision trees built with the random ensemble method, we find that the correlation between halo concentration and assembly history is tight: more than 60% of the variance in halo concentration can be explained by assembly history alone.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
