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.
INK scores improve OOD detection for classifiers.
Proposes and evaluates three diagnostic graphics for probabilistic classifiers.
New metric scores perturbations across populations, not cells, improving model comparison.
A new framework separates classifier calibration and discrimination.
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier's predictions should and should not be trusted has received …
Score-based generative models achieve state-of-the-art classification accuracy on CIFAR-10.
Improved image generation quality using closed-form discriminator guidance in diffusion models.
We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted …
We consider the two-group classification problem and propose a kernel classifier based on the optimal scoring framework. Unlike previous approaches, we provide theoretical guarantees on the expected risk consistency of the method. We also allow for feature selection by imposing structured sparsity using weighted kernel…
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…
The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories. Computing this partition function, , is then linear in the number of categories, which is problematic as real-world problem sets continue to grow in categorical types, such as …
We introduce a novel approach for training adversarial models by replacing the discriminator score with a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. W…
Proposes R2LDA for improved LDA classifier performance.
A novel method selects genes for high-dimensional gene expression data with class imbalance.
DFSOS improves sparse discriminant analysis for high-dimensional data.
Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally…
New method computes discriminative classifiers from generative models.
Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of a GAN trained on the same dataset. We show that the discriminator con…
Study compares multivariate scoring rules for distribution forecasts.
New methods for scoring function decomposition improve forecast evaluation.
CDAM improves attention maps for ViTs, making them more class-sensitive.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
New research shows input-gradients can be manipulated without changing model's core function, challenging their use for model interpretation.
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee f…
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.
Proposes GM Score to evaluate GANs considering diversity, disentanglement, and discriminability.
Improves GANs by sampling from an energy-based model induced by discriminator scores.
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions. Discrimination functions of undirected Markov network classifiers can thus be characterized by sets of linear constraints. These constraints are represented by a second order fi…
Generative classifier derived from any discriminative classifier rejects illegal inputs.
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns dis…
Generative Cross-Entropy improves classification with fewer labels.
A new QDA classifier for high-dimensional data with spiked covariance.
Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original co…
The paper proposes a method to select clusters, models, and algorithms based on quadratic discriminant scores.
This short report describes our submission to the ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work has been accomplished by a team of researchers at the University of Dayton Signal and Image Processing Lab. Our proposed approach is computationally efficient are comb…
Nested Cavity Classifier (NCC) is a classification rule that pursues partitioning the feature space, in parallel coordinates, into convex hulls to build decision regions. It is claimed in some literatures that this geometric-based classifier is superior to many others, particularly in higher dimensions. First, we give …
New robustness metric helps select reliable classifiers.
Robust GQDA improves classification accuracy in non-Normal data.
PBN combines generative and discriminative capabilities in a neural network.
AdvAs improves GAN training by penalizing the generator based on discriminator gradients.
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discriminat…
Discriminative classifiers improve decision-making in SHM systems.
New models reduce discrimination in machine learning without sacrificing explanatory bias.
The study tests and optimizes fairness in credit scoring models.
We introduce a discriminative regression approach to supervised classification in this paper. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression models extends existing models such as r…
Generative classifiers show surprising human-like performance.