arXiv research

Proposes a method to improve class-conditional conformal prediction for many classes.

In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric techniq…

KCal calibrates deep networks by embedding logits in a metric space.

SJS model predicts label shifts in multinomial datasets.

New approach predicts event probabilities for better event detection.

While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to the same degree. Uncertainty methods are designed to provide an estimate of class probabilities when predicting class assignment. While there …

The article focuses on determining the predictive uncertainty of a model on the example of atrial fibrillation detection problem by a single-lead ECG signal. To this end, the model predicts parameters of the beta distribution over class probabilities instead of these probabilities themselves. It was shown that the desc…

This paper investigates two feature-scoring criteria that make use of estimated class probabilities: one method proposed by \citet{shen} and a complementary approach proposed below. We develop a theoretical framework to analyze each criterion and show that both estimate the spread (across all values of a given feature)…

Paper introduces a novel method for estimating model confidence in deep neural classifiers.

A new method detects changes in multivariate data using random forests.

Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision making, the main advantage of probabilistic models is their ability to represent uncertainty about predictions. In safety-critical applicatio…

PosCal training improves classification models by calibrating posterior probabilities.

This work presents a new classifier that is specifically designed to be fully interpretable. This technique determines the probability of a class outcome, based directly on probability assignments measured from the training data. The accuracy of the predicted probability can be improved by measuring more probability es…

Flexible evidential deep learning improves uncertainty quantification in machine learning.

The problem is sequence prediction in the following setting. A sequence x1,..., xn,... of discrete-valued observations is generated according to some unknown probabilistic law (measure) mu. After observing each outcome, it is required to give the conditional probabilities of the next observation. The measure mu belongs…

New method calibrates classifier probabilities with guaranteed coverage.

ECBMs unify concept-based interpretations in deep learning models.

New tools connect CP to GF inference for better probabilistic prediction.

Improves Active Learning by considering class imbalance and difficulty.

Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP). We show how using the TCP is more suited…

An imprecise SHAP method explains class probabilities with limited data.

In many applications, accurate class probability estimates are required, but many types of models produce poor quality probability estimates despite achieving acceptable classification accuracy. Even though probability calibration has been a hot topic of research in recent times, the majority of this has investigated n…

A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark …

Knowledge distillation improves model accuracy by mimicking teacher model probabilities.

New method calibrates multi-class predictions efficiently without sacrificing accuracy.

We extend Bayes' theorem for upper probabilities considering likelihood uncertainty.

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the ca…

Noise-Aware Conformal Prediction (NACP) calibrates CP for noisy labels.

Kandinsky conformal prediction expands conditional coverage guarantees.

In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emp…

This paper improves deep learning classifiers by integrating conformal prediction during training.

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…

Proposes a new method to minimize non-singleton predictions in conformal prediction.

Improves reliability of medical diagnosis uncertainty estimates.

For a broad variety of critical applications, it is essential to know how confident a classification prediction is. In this paper, we discuss the drawbacks of softmax to calculate class probabilities and to handle uncertainty in Bayesian neural networks. We introduce a new kind of prediction layer called radial predict…

The paper develops bounds for predictive values in binary classification.

An accurate model of a patient's individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (e.g., from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (e.g., the Gail model, predicting 5 year prob…

TS improves class coverage but reduces CP set size, offering a trade-off.

Proposes novel wSVMs for sparse learning and accurate probability estimation.

New approach calibrates predictions for better decision-making.

Develops geometric framework for uncertainty-aware multi-class classification.

A new method for measuring prediction uncertainty in classifiers.

Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. How one measures calibration remains a challenge: expected calibration error, the most popular metric, ha…

Most classification methods provide either a prediction of class membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be described as "risk" of belonging to a "special" class . When the required output is a set of ordinal-risk groups, a discr…

Survey on calibration in machine learning, viewing it as indistinguishability.

New method calibrates neural network predictions for better reliability.

ICP provides interval predictions with high confidence coverage.

We consider the following conditional linear regression problem: the task is to identify both (i) a $k$-DNF condition $c$ and (ii) a linear rule $f$ such that the probability of $c$ is (approximately) at least some given bound $μ$, and $f$ minimizes the $\ell_p$ loss of predicting the target $z$ in the distribution of …