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.
The paper shows over-confidence in models isn't just due to over-parametrization.
Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident predictions, it is unclear how model confidence is impacted by the variations in the data, such as label noise or class size. In this paper, w…
GUST framework improves self-training by estimating node uncertainty and generating pseudo-labels.
Bayesian methods improve OoD detection in deep networks.
Following a long tradition of physicists who have noticed that the Ising model provides a general background to build realistic models of social interactions, we study a model of financial price dynamics resulting from the collective aggregate decisions of agents. This model incorporates imitation, the impact of extern…
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned via a greedy optimization procedure. This restri…
Paper introduces a method to learn physics between digital twins using imperfect models.
We study a dynamical Ising model of agents' opinions (buy or sell) with coupling coefficients reassessed continuously in time according to how past external news (magnetic field) have explained realized market returns. By combining herding, the impact of external news and private information, we test within the same mo…
The paper proposes a method to produce well-calibrated predictions in regression tasks using maximum mean discrepancy.
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction …
Post-hoc calibration improves uncertainty under domain shift.
Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble meth…
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer. On non-neural models the exist…
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there ha…
Blade uses diffusion priors to accurately and calibratedly infer complex systems.
Density-Softmax improves uncertainty estimation and robustness without sampling, reducing model size and latency.
Bayesian bandits misspecification affects UX optimization, revealing new models.
This paper reviews metrics to assess AI model calibration accuracy.
The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing…
Characterizes uncertainty in high-dimensional linear classification models.
Deep ensembles don't necessarily improve calibration in low data regimes.
Bayesian neural networks improve uncertainty quantification with unlabelled data.
A new AI framework reduces costs and improves performance.
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization. Whilst the current sparse spectrum methods provide desired approximations for regression problems, it is observed that this particular form of sparse approximations generates an overconfident GP, i.e. it produc…
Enhances out-of-domain calibration of neural networks.
Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has been shown to be a surprisingly effective method of data augmenta…
The study examines label smoothing to improve confidence calibration in fine-tuned LLMs.
CAT improves domain adaptation for fault diagnosis by calibrating teacher network predictions.
Study improves CNN medical image segmentation accuracy and reliability.
Proposes deep quantile regression for uncertainty estimation in lesion detection.