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.
New conformal prediction methods for long-tailed classification problems.
New framework for learning with class-conditional multi-label noise.
YuruGAN generates yuru-chara images using GANs and clustering for small datasets.
SNS-GAN integrates class labels into generative models for images and time series.
Plug-and-play multimodal controller improves class-conditional image generation.
Proposes a method to improve class-conditional conformal prediction for many classes.
We introduce advocacy learning, a novel supervised training scheme for attention-based classification problems. Advocacy learning relies on a framework consisting of two connected networks: 1) Advocates (one for each class), each of which outputs an argument in the form of an attention map over the input, and 2) a …
We present a simple generative framework for learning to predict previously unseen classes, based on estimating class-attribute-gated class-conditional distributions. We model each class-conditional distribution as an exponential family distribution and the parameters of the distribution of each seen/unseen class are d…
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate cla…
In this draft, which reports on work in progress, we 1) adapt the information bottleneck functional by replacing the compression term by class-conditional compression, 2) relax this functional using a variational bound related to class-conditional disentanglement, 3) consider this functional as a training objective for…
We show how to compute lower bounds for the supremum Bayes error if the class-conditional distributions must satisfy moment constraints, where the supremum is with respect to the unknown class-conditional distributions. Our approach makes use of Curto and Fialkow's solutions for the truncated moment problem. The lower …
New method improves transfer and robustness of supervised contrastive learning.
New method removes pseudo-label bias for unsupervised domain adaptation.
Generative models assess quality on time-series data using ITS and FITD.
We investigate the problem of classification in the presence of unknown class-conditional label noise in which the labels observed by the learner have been corrupted with some unknown class dependent probability. In order to obtain finite sample rates, previous approaches to classification with unknown class-conditiona…
We associate determinant lines to objects of the extended abelian category built out of a von Neumann category with a trace. Using this we suggest constructions of the combinatorial and the analytic L^2 torsions which, unlike the work of the previous authors, requires no additional assumptions; in particular we do not …
DeepCCG adapts classifiers to representation shifts in one step.
Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.
We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN). The discriminator of an LD-GAN is trained to maximize the linear separability between distributions of hidden representations of generated and targeted samples, while the …
In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are …
New method uses label-weighted conformal prediction for macro-coverage guarantees in classification.
Alternative hypothesis tests for class-conditional noise using local maximum likelihood.
Study reveals differences in label shift problem difficulty in supervised vs. unsupervised settings.
Method reduces categorical data to lower dimensions using density matrices.
Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enou…
Paper proposes a new method for supervised manifold learning using random forest proximities.
Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a nove…
Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or otherwise corrupted images based on a class-conditional reconstruction of the input. To specifically attack our detection mechanism, we pro…
Optimal transport aligns source and target distributions for domain adaptation.
Well-known for its simplicity and effectiveness in classification, AdaBoost, however, suffers from overfitting when class-conditional distributions have significant overlap. Moreover, it is very sensitive to noise that appears in the labels. This article tackles the above limitations simultaneously via optimizing a mod…
Local ancestry inference (LAI) allows identification of the ancestry of all chromosomal segments in admixed individuals, and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and precision medicine to genome-wide association studies. In recent years, many LAI techniques have…
Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional sparse coding algorithms and its manifold regularized variants (graph sparse coding and Laplacian sparse coding), learn th…
Unified view of label shift estimation methods.
We prove Gray--Moser stability theorems for complementary pairs of forms of constant class defining symplectic pairs, contact-symplectic pairs and contact pairs. We also consider the case of contact-symplectic and contact-contact structures, in which the constant class condition on a one-form is replaced by the conditi…
This work introduces an efficient method to sample high-quality images from conditional GANs.
We propose a way of computing 4-manifold invariants, old and new, as chiral correlation functions in half-twisted 2d theories that arise from compactification of fivebranes. Such formulation gives a new interpretation of some known statements about Seiberg-Witten invariants, such as the basic class …
Enhanced Sampling Scheme improves masked generative modeling.
RFM improves CNFs by adding a boundary constraint term and matching velocity fields.
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of t…
Optimal projections enhance Naive Bayes classification.
New approach tackles class imbalance in long-tailed datasets using domain adaptation techniques.
The study sets limits on how robust classifiers can be against adversarial attacks.
Entropy tracking reveals class commitment transitions in diffusion models.
Algorithm improves binary classification of biased grouped data.
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a d…
New method improves domain generalization by matching object representations.
The paper examines -torsion in fibration cases relaxing standard conditions.
This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.