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.
Paper improves deep learning for instance-level classification from label proportions.
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known. The task is to learn a model to predict the class labels of the individual instances. LLP has broad applications in political science, market…
Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.
Paper proposes a new model using consistency regularization for learning from label proportions.
Generative adversarial networks learn from label proportions without distributional restrictions.
The paper explores learning from label proportions, showing differences in efficiency between LLP and PAC learning.
New learning rules achieve optimal sample complexity for weakly supervised classification.
We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain size, and only the proportions of each label within a given bag are known. This is a common situation in cases where per-data labeling is lengt…
We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or SVM, which explicitly models the latent unknown instance labels together with the known group …
New framework for weakly supervised learning from label proportions.
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for understanding DNN generalization for such datasets, by investigating the dimensionality of the deep representation subspace of training samples. …
Transfer learning improves sentiment classification using XR framework.
This work efficiently learns linear threshold functions from label proportions using Gaussian distributions.
New attacks reveal training label proportions in federated learning.
RLSbench benchmarks domain adaptation under label proportion shifts, revealing widespread failures and proposing a two-step meta-algorithm.
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare…
We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume on…
Study improves binary classification with multiple corrupted samples.
New method for estimating class proportions in open-set label shift data.
Easyllp simplifies LLP, achieving low task loss at individual instance level.
New framework for learning from various weak supervision types.
This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set…
A new large-scale tabular benchmark for Learning from Label Proportions.
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier under mild conditions; however, it results in biased empirical estimates of the classifier performance. In this work, we show that the typi…
ETM models improve efficiency in semi-supervised logistic regression.
Proposes a method to create predictive sets from partially labeled data.
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this …
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 research shows that binary classification can be done with noisy data, but only if there are clean samples available.
Active learning method balances bias and variance under class imbalance.
We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. As opposed to unsupervised initialisation followed by supervised fine tuning, our method takes advantage of both unlabelled and labelled data…
Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.
Paper tackles dynamic label shift in online learning, achieving optimal performance.
Discrepancy between training and testing domains is a fundamental problem in the generalization of machine learning techniques. Recently, several approaches have been proposed to learn domain invariant feature representations through adversarial deep learning. However, label shift, where the percentage of data in each …
Study of deep linear neural networks with proportional width and depth.
Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many "weakly supervised learning" problems like learning with positive and unlabelled samples, learning with label no…
This work tackles robust multi-source domain adaptation under label shift.
New method uses unlabeled data to estimate intercept in case-control logistic regression.
Study reveals pervasive label errors in test sets, affecting machine learning benchmarks.
A method for selecting pseudo-labeled data in semi-supervised learning using generalized Bayes and soft revision.
Multi-domain image-to-image translation is a problem where the goal is to learn mappings among multiple domains. This problem is challenging in terms of scalability because it requires the learning of numerous mappings, the number of which increases proportional to the number of domains. However, generative adversarial…
Kernel density matrices simplify probabilistic deep learning.
New method improves deep learning models robustness to label noise.
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we…
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
Optimal transport aligns source and target distributions for domain adaptation.
Extends risk control to adaptive data collection, anytime-valid guarantees.
Objective: Using traditional approaches, a Brain-Computer Interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g.~by transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch …