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Found 11 papers, 2 papers with code
Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessments
We compared accuracy with state-of-the-art (SOTA) distilled models, TinyBERT, and artificial neural network (ANN) models.
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data
When applied to European power grid consumption data, the proposed leverage score based sampling methods outperform the benchmark sampling method in online estimation and prediction.
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation
Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator.
Large-scale optimal transport map estimation using projection pursuit
We theoretically show the proposed dimension reduction method can consistently estimate the most ``informative'' projection direction in each iteration.
Sufficient dimension reduction for classification using principal optimal transport direction
The proposed method, named principal optimal transport direction (POTD), estimates the basis of the SDR subspace using the principal directions of the optimal transport coupling between the data respecting different response categories.
A Review on Modern Computational Optimal Transport Methods with Applications in Biomedical Research
Optimal transport has been one of the most exciting subjects in mathematics, starting from the 18th century.
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms
In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem.
Minimax Nonparametric Two-sample Test under Smoothing
Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.
Optimal Subsampling for Large Sample Logistic Regression
In this paper, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression.
Optimal Subsampling Approaches for Large Sample Linear Regression
For unweighted estimation algorithm, we show that its resulting subsample estimator is not consistent to the full sample OLS estimator.
A Statistical Perspective on Algorithmic Leveraging
A detailed empirical evaluation of existing leverage-based methods as well as these two new methods is carried out on both synthetic and real data sets.
