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 study quantifies and compares aleatoric and epistemic discrimination in ML models.
Discriminator guidance improves autoregressive diffusion models for generating molecular graphs.
New method computes discriminative classifiers from generative models.
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…
We study Bayesian discriminative inference given a model family $p(c,\x, θ)$ that is assumed to contain all our prior information but still known to be incorrect. This falls in between "standard" Bayesian generative modeling and Bayesian regression, where the margin $p(\x,θ)$ is known to be uninformative about $p(c|\x,…
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.
In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space. The mixture model can address this issue, although it leads to an increase in the number of parameters. We propose a sparse classifier base…
We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization. We find that the model capacity of the discriminator has a significant effect on the generator's model quality, and that the generator's poor performance coincides with…
Generalized dual discriminator GANs improve upon traditional GANs by using two discriminators and a flexible loss function.
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator. We gradually change each discriminator's task from distinguishing between real and fake samples to disc…
DNLL loss improves deep LDA accuracy and consistency.
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. T…
Paper introduces a new method to improve GANs by leveraging the discriminator's energy.
New findings show fixed-kernel discriminators are weaker than feature-learning ones.
Smart Bayes integrates generative and discriminative features for improved classification.
Discriminative clustering uses mutual information to cluster data.
The study tackles indirect discrimination in insurance pricing models.
We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptoti…
In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods. Moreover, it can be directly used to define a class-specific probabilis…
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biolog…
Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.
GNNs improve graph signal discrimination by adding nonlinearities.
Paper introduces a method to improve survival model calibration without sacrificing discrimination.
Paper formalizes anti-discrimination law in automated systems.
FairUDT uses uplift decision trees to detect and mitigate discrimination in training data.
New method improves OOD detection by integrating diffusion models into discriminator models.
Optimized GAN discriminator using polyharmonic interpolation.
New method reduces indirect discrimination in insurance risk models.
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label set…
Improved LDA method for better classification and dimensionality reduction.
This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimisation difficult. We also show that this curvature becomes more extreme as the maximal ke…
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…
Neural network based generative models with discriminative components are a powerful approach for semi-supervised learning. However, these techniques a) cannot account for model uncertainty in the estimation of the model's discriminative component and b) lack flexibility to capture complex stochastic patterns in the la…
New algorithms improve robust estimation in contaminated Gaussian models.
The study uses a multi-armed bandit model to analyze and mitigate hiring discrimination.
As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation. According to previous studies, device level consumption could lead to significant freshwater conservation. Existing water disaggregation methods focus on learning the signatures for appliances; how…
Locally Valid and Discriminative prediction intervals for deep learning models.
The paper addresses instability in KL divergence estimation using a neural network discriminator.
In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter re…
Improved language models by incorporating statistical discriminators.
The discriminator from generative adversarial nets (GAN) has been used by researchers as a feature extractor in transfer learning and appeared worked well. However, there are also studies that believe this is the wrong research direction because intuitively the task of the discriminator focuses on separating the real s…
A new method quantifies feature-map discriminativeness for efficient pruning of deep neural networks.
Simplified GAN model shows how discriminator improves generalization.
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-lik…
Proposes improved classification via transfer learning with regularized linear discriminant analysis.
Domain adaptation is essential to enable wide usage of deep learning based networks trained using large labeled datasets. Adversarial learning based techniques have shown their utility towards solving this problem using a discriminator that ensures source and target distributions are close. However, here we suggest tha…
Improved image generation quality using closed-form discriminator guidance in diffusion models.
Quantum machine learning model for binary classification.