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.
Distillation improves simple models by approximating complex labels.
The law of total probability may be deployed in binary classification exercises to estimate the unconditional class probabilities if the class proportions in the training set are not representative of the population class proportions. We argue that this is not a conceptually sound approach and suggest an alternative ba…
Knowledge distillation improves model accuracy by mimicking teacher model probabilities.
A new method for adapting to label shifts using class probability matching.
This paper proposes a methodology for host-based anomaly detection using a semi-supervised algorithm namely one-class classifier combined with a PCA-based feature extraction technique called Eigentraces on system call trace data. The one-class classification is based on generating a set of artificial data using a refer…
In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Based on existing literat…
Study post-hoc Learning to Defer using density-ratio losses.
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we propose a geometric regularization technique to find the submanifold corresponding to a robust estimator of the class probability . The regularization term meas…
An imprecise SHAP method explains class probabilities with limited data.
Paper introduces a novel method for estimating model confidence in deep neural classifiers.
A new method detects changes in multivariate data using random forests.
FJS method improves multinomial classification accuracy.
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…
Flexible evidential deep learning improves uncertainty quantification in machine learning.
New algorithms optimize metrics for binary classification with class imbalance.
A new metric for uncertainty quantification using class collisions.
Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been proposed to limit the information provided to the adversary by omitting probability scores, significantly impacting the utility of the provid…
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that - Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; un…
This paper develops convex surrogates for optimizing the multi-label F-measure.
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 …
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…
Deep learning models are vulnerable to external attacks. In this paper, we propose a Reinforcement Learning (RL) based approach to generate adversarial examples for the pre-trained (target) models. We assume a semi black-box setting where the only access an adversary has to the target model is the class probabilities o…
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…
We establish linear regret bounds for convex smooth losses using Fenchel-Young losses.
Paper develops proper, lower-bounded losses for weakly supervised classification.
Proposes novel wSVMs for sparse learning and accurate probability estimation.
New uniqueness concept for adversarial Bayes classifier.
Empirical Bayes rates via variational approximations and prior decomposition.
The majority of traditional classification ru les minimizing the expected probability of error (0-1 loss) are inappropriate if the class probability distributions are ill-defined or impossible to estimate. We argue that in such cases class domains should be used instead of class distributions or densities to construct …
The study compares clustering risk in Hidden Markov and i.i.d. models, showing the Bayes classifier is nearly optimal.
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but a…
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…
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…
A-GPS learns to generate Pareto sets efficiently with user preferences.
KCal calibrates deep networks by embedding logits in a metric space.
Researchers prove NP-hardness of learning parameter-bounded Bayes nets.
Deep learning improves Bayes factor computation for likelihood-free models.
Meta-learning bounds derived using PAC-Bayes theory for improved generalization.
We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider three cases for a true model: (1) a smooth decision boundary, (2) smooth conditional class probability, and (3) the margin condition (i.e., t…
PAC-Bayes framework fails on simple 1D linear classification task.
Improves GAN-based semi-supervised learning with consistency regularization.
PAC-Bayes bound requires prior to place mass on high-performing predictors.
New estimators outperform maximum likelihood without hyper-parameter estimation.
Paper introduces Generalized Naive Bayes for better data fitting.
Smart Bayes integrates generative and discriminative features for improved classification.
PFNs pre-train models on simulated data to predict class probabilities.
As part of autonomous car driving systems, semantic segmentation is an essential component to obtain a full understanding of the car's environment. One difficulty, that occurs while training neural networks for this purpose, is class imbalance of training data. Consequently, a neural network trained on unbalanced data …
New method reduces computational cost for estimating PAC-Bayes bounds.