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Found 7 papers, 4 papers with code
Multicalibration for Confidence Scoring in LLMs
This paper proposes the use of "multicalibration" to yield interpretable and reliable confidence scores for outputs generated by large language models (LLMs).
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantification in deep learning.
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information.
Causal Bias Quantification for Continuous Treatments
We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models.
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems.
A Stein variational Newton method
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space.
