A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
The paper corrects Bayesian neural network approximations to improve decision quality.
This paper establishes the asymptotic consistency of the {\it loss-calibrated variational Bayes} (LCVB) method. LCVB was proposed in~\cite{LaSiGh2011} as a method for approximately computing Bayesian posteriors in a `loss aware' manner. This methodology is also highly relevant in general data-driven decision-making con…
Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights. However, this approximation is without knowledge of the final application, and therefore cannot guarantee optimal predictions for a given task. To make mo…
Early stopping improves neural networks' performance on binary classification tasks.
New loss function calibrates WW-hinge loss for multiclass SVM.
Proposes CDTD, a diffusion model for mixed-type tabular data.
Unified approach for optimizing predictions in linear programming and inverse problems.
In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for easily converting bounds for the surrogate risk (which can be computed through well-known methods) into bounds for the true risk, the probabili…
A new concordance loss improves model performance and reliability in survival prediction.