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Found 8 papers, 2 papers with code
Language Agnostic Code Embeddings
Recently, code language models have achieved notable advancements in addressing a diverse array of essential code comprehension and generation tasks.
Locally Differentially Private Document Generation Using Zero Shot Prompting
Numerous studies have highlighted the privacy risks associated with pretrained large language models.
Rieoptax: Riemannian Optimization in JAX
We present Rieoptax, an open source Python library for Riemannian optimization in JAX.
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
In that spirit, the only geometric statistical query for which a differential privacy mechanism has been developed, so far, is for the release of the sample Fr\'echet mean: the \emph{Riemannian Laplace mechanism} was recently proposed to privatize the Fr\'echet mean on complete Riemannian manifolds.
Shrinkage Estimation of Higher Order Bochner Integrals
We propose estimators that shrink the $U$-statistic estimator of the Bochner integral towards a pre-specified target element in the Hilbert space.
Quantile Regularization : Towards Implicit Calibration of Regression Models
We provide a detailed formal analysis of the \emph{side-effects} of Isotonic Regression when used for regression calibration.
Temperature Scaling for Quantile Calibration
Deep learning models are often poorly calibrated, i. e., they may produce overconfident predictions that are wrong, implying that their uncertainty estimates are unreliable.
Quantile Regularization: Towards Implicit Calibration of Regression Models
It is therefore desirable to have models that produce predictive uncertainty estimates that are reliable.
