Develops a method to discriminate between competing models using Gaussian process surrogates.
problem Discriminating between competing models when data is insufficient and models are non-analytical.
method Introduces Gaussian process surrogates to extend design of experiments methods to non-analytical models.
result Extends design of experiments methods to non-analytical models in a computationally efficient manner.
A new PCA method for analyzing multiple datasets.
problem Analyzing multiple datasets for discriminative features.
method Discriminative PCA (dPCA) for feature extraction.
result dPCA optimally recovers target data components.
Corrects group discrimination in scores with minimal individual error.
problem Addressing group discrimination within a score function.
method Analytical solution for two populations, linear programming for n populations.
result An approximate solution with high precision can be computed.
Develops a novel approach for discriminative data analysis.
problem Challenges of analyzing multiple datasets using PCA.
method Solves a generalized eigenvalue problem by performing SVD once.
result Establishes optimality in the least-squares sense.
Optimized GAN discriminator using polyharmonic interpolation.
problem Optimizing the discriminator in GANs with higher-order gradient regularization.
method Polyharmonic interpolation and variational calculus.
result The optimal discriminator is a polyharmonic radial basis function.
A new DRM classifier for high-dimensional and imbalanced data.
problem Classification in high-dimensional and imbalanced data.
method Discriminative regression approach with iterative algorithms.
result Superior performance compared to state-of-the-art classifiers.
The paper introduces a method to eliminate latent discrimination in predictive models.
problem Controlling for latent discrimination in predictive models.
method Defining a new fairness criterion inspired by omitted variable bias, and a training strategy that includes sensitive features during training and excludes them during testing.
result A simple yet effective strategy to eliminate latent discrimination in predictive 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 t…
Extract useful information from football players' trajectories.
problem Automatic processing of two-dimensional positional data during matches.
method Newtonian mechanics, Kalman filter, Generative Adversarial Nets, Variational Autoencoders, Discriminator network.
result Deep generative models can learn underlying structure and statistics of trajectories.
Paper studies compactifications of Higgs bundles and self-duality equations.
problem Compactification of Hitchin moduli space and Higgs bundles.
method Analyzes maps between algebraic and analytic compactifications.
result Map between compactifications fails to be continuous at boundary over discriminant locus.
Develops methods for integrating multivariate normals and computing classification measures.
problem Computing performance of multivariate normal models is challenging due to lack of general analytical expressions.
method Mathematical results and open-source software for integrating and analyzing multivariate normal distributions.
result Provides tools for calculating classification errors, discriminability, and reliability.
Analytic torsion behavior studied for degenerating manifolds with equivariant bundles.
problem Behavior of analytic torsion for degenerating manifolds with equivariant bundles.
method Asymptotic expansion of equivariant analytic torsion, Quillen metrics, L2-metrics, Bott-Chern classes.
result Leading term of analytic torsion has logarithmic singularity, subdominant term has loglog-type singularity.
New activation functions improve neural network performance for exoplanet classification.
problem Optimizing neural network performance for exoplanet classification with hard attribute removal.
method Investigation of novel activation functions using ODE and fixed point theory, followed by empirical validation.
result Optimal neural network performance achieved without tuning, comparable to traditional functions.
Enhances deep learning robustness to noise without sacrificing clean data accuracy.
problem Robustness of deep neural networks to input noise.
method Discriminative loss at penultimate layer and class-wise feature alignment with Gaussian noise.
result Improves robustness to various perturbations without degrading clean data accuracy.
The scaled complex Wishart distribution is a widely used model for multilook full polarimetric SAR data whose adequacy has been attested in the literature. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some type of dissimilarity measure…
DEVDAN adapts to changing data streams by dynamically adding and removing hidden units.
problem Fixed DAE network capacity limits adaptability to rapidly changing environments.
method DEVDAN features an open structure with dynamically adjustable hidden units.
result DEVDAN outperforms state-of-the-art methods on ten datasets.
ELICA helps analysts extract relevant information during elicitation meetings.
problem Challenges in eliciting requirements due to analyst's lack of domain knowledge.
method ELICA uses a novel information extraction algorithm combining WFSTs and SVMs, presented in an interactive GUI.
result ELICA effectively extracts relevant information in real-time and facilitates interactive and dynamic process.
Visualizes classification results with class maps.
problem Label bias in classification predictions.
method Class maps reflecting probability, distance, and mislabeling likelihood.
result Insight into classification results and data structure.
Deep IDA integrates multi-view data to classify COVID-19 severity, identifying molecular signatures.
problem Understanding the complexity of COVID-19 severity from multi-view clinical and molecular data.
method Deep IDA learns nonlinear projections to maximize view associations and class separations, with feature ranking.
result Deep IDA outperforms other methods in classifying COVID-19 severity and identifies interpretable molecular signatures.
New model combines shape and feature-based measures for better time series classification.
problem Limited approaches in time series classification lead to poor results for some classes.
method Proposes a new model that automatically decides between shape and feature-based measures.
result Improves classification accuracy statistically significantly on real-world datasets.
Paper tackles discrimination in predictions using causal modeling.
problem Mathematical guarantee for non-discrimination in predictions.
method Causal modeling to define discrimination, bounding prediction discrimination probability.
result Discrimination in predictions can still exist even if training data is free of discrimination.
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
problem Sources of discrimination in ML models and their impact on performance.
method Quantifying aleatoric and epistemic discrimination using statistical experiments and model accuracy.
result State-of-the-art fairness interventions are effective at removing epistemic discrimination but not aleatoric discrimination in datasets with missing values.
A fast cross-validation method for high-dimensional data.
problem High computational cost of least-squares models in high-dimensional datasets.
method Analytical approach for k-fold cross-validation without explicit model training.
result Up to 10,000x faster than standard approach in high-dimensional data.
Discriminator guidance improves autoregressive diffusion models for generating molecular graphs.
problem Improving the accuracy of autoregressive diffusion models for generating molecular graphs.
method Deriving ways to use a discriminator with a pretrained generative model in the discrete case, including optimal and sub-optimal scenarios.
result Using a discriminator can correct pretrained models and improve exact sampling from the data distribution.
This paper proposes a curriculum learning method for GANs using multiple discriminators.
problem Training GANs with sufficient convergence conditions and avoiding mode collapse.
method A framework for training the generator against an ensemble of discriminator networks, formalized in the full-information adversarial bandit framework.
result Our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
problem Limited effectiveness of linear discriminant analysis in capturing nonlinear features.
method Study of nonlinear embeddings in kernel discriminant analysis using polynomial and Gaussian kernels, solving generalized eigenvalue problems.
result Polynomial and Gaussian discriminants capture class differences through population moments and randomized projections.
Lower-dimensional video discriminators improve GAN performance.
problem High curvature in unconstrained video discriminator loss surfaces.
method Proposed Lower-Dimensional Video Discriminators (LDVD GANs).
result LDVD GANs double Temporal-GAN performance and achieve state-of-the-art performance.
Discriminative clustering uses mutual information to cluster data.
problem Clustering data into cohesive groups.
method Discriminative clustering using mutual information.
result Mutual information has been a cornerstone of discriminative clustering.
Pairwise discriminators stabilize adversarial training by ensuring the generator's alignment is preserved.
problem Stability issues in adversarial training when using sub-optimal discriminators.
method Introducing a family of objectives using pairwise discriminators, ensuring the generator's alignment is preserved.
result Only the generator needs to converge, and the alignment is preserved with any discriminator.
Machine learning predicts properties of number fields with high accuracy.
problem Predicting properties of algebraic number fields.
method Training machine learning algorithms on various coefficients or polynomials of number fields.
result Machine learning can distinguish between real quadratic fields with high precision and predict properties of Galois extensions.
PBN combines generative and discriminative capabilities in a neural network.
problem Combining generative and discriminative capabilities in neural networks.
method Convolutional PBN, sharing FF-NN embodiment, combining generative and discriminative qualities.
result PBN shows excellent qualities from either generative or discriminative viewpoint.
Study on GANs under and overfitting using discriminator unseen data.
problem Understanding and mitigating under and overfitting in GANs.
method Using discriminators unseen by the generator to measure generalization.
result Model capacity of the discriminator affects generator quality; large discriminator capacities do not prevent overfitting.
Paper analyzes the tradeoff between a discriminator's ability to identify true distribution and its ability to generalize.
problem Discrimination-generalization tradeoff in GANs.
method Analyzes the linear span of discriminator sets and their relationship to generalization and discriminative ability.
result Discriminator sets with dense linear span are both discriminative and generalizable.
Improved GAN sampling by collaborating discriminator and generator.
problem Loss of discriminator information during standard GAN sampling.
method Collaborative sampling between generator and discriminator, gradient-based updates.
result Generated samples are closer to real data distribution.
Study infinite Euclidean distance discriminants of algebraic varieties.
problem Understanding the structure of data points with infinitely many critical points in Euclidean distance correspondence.
method Developed computer code to compute discriminants and proved properties of fibers.
result Infinite Euclidean distance discriminants contain all data points with infinitely many critical points for the nearest-point problem.
Discriminators can be good feature extractors despite their task focus.
problem Discriminators' features are often considered useless for downstream tasks.
method Theoretical analysis and feature space examination to understand discriminator's role.
result Discriminator features are robust and can prevent mode collapse, making them useful for transfer learning.
Rob-GAN combines generator, discriminator, and adversarial attack for improved robustness and quality.
problem Improving robustness and quality of GAN-generated images under adversarial attacks.
method Rob-GAN framework that jointly optimizes generator and discriminator in the presence of adversarial attacks.
result Rob-GAN improves convergence speed, image quality, and robustness of discriminators under strong adversarial attacks.
Generalized dual discriminator GANs improve upon traditional GANs by using two discriminators and a flexible loss function.
problem Mode collapse in GANs.
method Introducing dual discriminator α-GANs and extending the approach to arbitrary functions. result The approach reduces the optimization problem to a linear combination of an f-divergence and a reverse f-divergence. GNNs improve graph signal discrimination by adding nonlinearities.
problem Improving graph signal discrimination in physical networks.
method Analyzing the discriminability of GNNs and their relation to graph filter banks.
result GNNs are at least as discriminative as linear graph filter banks.
Paper proposes distributed sparse multicategory discriminant analysis for classification.
problem Sparse multicategory classification with distributed data.
method Convex formulation, distributed setting, invariant discriminant subspace recovery.
result Distributed sparse multicategory linear discriminant analysis performs as good as centralized version after a few rounds of communications.
Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies th…
New method computes discriminative classifiers from generative models.
problem Discriminative vs generative classifiers are often seen as distinct, but this work shows they can be equivalent.
method General theoretical result showing generative classifiers can be computed discriminatively.
result Bayesian Maximum Posterior classifier from generative models matches discriminative classifier definition.
Proposes a curriculum-based dropout discriminator for domain adaptation.
problem Improving domain adaptation using deep learning networks trained on large labeled datasets.
method Introduces a curriculum-based dropout discriminator that gradually increases sample variance and uses reverse gradients to align source and target feature representations.
result The proposed model outperforms state-of-the-art results in domain adaptation tasks.
Study combines expert advice to avoid discrimination without violating equalized error rates.
problem Combining expert advice to avoid discrimination without violating equalized error rates.
method Running separate instances of the classical multiplicative weights algorithm for each group.
result Even for equalized error rates, algorithms with stronger performance guarantees than multiplicative weights cannot preserve non-discrimination.
This paper tackles GAN instability by dualizing the discriminator.
problem GAN training instability due to the maximin formulation.
method Dualizing the discriminator to reformulate the saddle point objective into a maximization problem.
result The dualing GAN approach removes instability for linear discriminators and provides an alternative for nonlinear discriminators.
Proposes a sparse classifier for discriminative Gaussian Mixture Models.
problem Softmax-based discriminative models assume unimodality, leading to parameter redundancy.
method Sparse Bayesian learning for GMM-based discriminative model, reducing parameters and complexity.
result The SDGM outperforms existing softmax-based discriminative models.
Paper introduces a new probabilistic model for class-specific discriminant analysis.
problem Lack of multi-modal structure consideration in existing class-specific methods.
method Formulates a probabilistic model that incorporates multi-modal negative class structure.
result Proposed model can be directly used for class-specific probabilistic classification.