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.
Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.
New method adapts to structural shifts in graph data for better label prevalence estimation.
Estimates disease prevalence using non-ignorable missing data in health surveys.
Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.
The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the la…
Study reveals pervasive label errors in test sets, affecting machine learning benchmarks.
Paper explores how unsupervised learning can be understood through linear algebra concepts.
This study connects prevalence and machine learning for diagnostic testing.
Study enhances classifier robustness against noisy labels.
Novel unsupervised scheme for highly imbalanced and overlapping datasets.
Classification is the task of predicting the class labels of objects based on the observation of their features. In contrast, quantification has been defined as the task of determining the prevalences of the different sorts of class labels in a target dataset. The simplest approach to quantification is Classify & Count…
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on …
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy lab…
Geometry-aware KDE model improves multiclass quantification.
HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.
Label noise in adversarial training leads to robust overfitting, explained and mitigated.
Enhances image classification by integrating semantic hierarchy into CNN models.
One-hot encoding is a labelling system that embeds classes as standard basis vectors in a label space. Despite seeing near-universal use in supervised categorical classification tasks, the scheme is problematic in its geometric implication that, as all classes are equally distant, all classes are equally different. Thi…
NoiseRank reduces label noise without supervision, improving classification accuracy.
Improves SSL with doubly robust estimation of unlabeled class distribution.
The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate w…
The thesis introduces methods to use semantic hierarchy in image classification.
End-to-end approach for weak supervision improves downstream model performance.
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods hav…
Usually considered as a classification problem, entity resolution (ER) can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most notably deep neural networks), which require lots of accurately labeled training data.…
New framework learns from partial feedback in multi-label tasks.
This paper considers the challenge of evaluating a set of classifiers, as done in shared task evaluations like the KDD Cup or NIST TREC, without expert labels. While expert labels provide the traditional cornerstone for evaluating statistical learners, limited or expensive access to experts represents a practical bottl…
In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealin…
Labeling of sequential data is a prevalent meta-problem for a wide range of real world applications. While the first-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling, the basic model does not show satisfying performance when it is directly applied to real world probl…
Proposes a method to improve rare event prediction in healthcare.
Method counters noisy labels by discounting distant samples.
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
The study examines when to trust confidence thresholding in pseudo-labelling regression.
The paper discusses thresholds and bounds for accuracy in binary classification systems.
Bayesian framework estimates label shift for improved classifier performance.
Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to sc…
Continuous Sweep improves binary quantifier performance.
Fake news may be intentionally created to promote economic, political and social interests, and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake news is an emerging problem that has become extremely prevalent during the last few years. Most existing works on this topic focus on man…
A new method for robust training under label noise using weighted gradient descent.
RAEUFS selects features from data without labels, improving robustness to outliers.
Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to ground truth labels. Unfortunately, in practice, labels may only exist conditional on certain decisions---if a loan is denied, there is not eve…
Point estimation of class prevalences in the presence of data set shift has been a popular research topic for more than two decades. Less attention has been paid to the construction of confidence and prediction intervals for estimates of class prevalences. One little considered question is whether or not it is necessar…
Bayesian method corrects bias in imbalanced datasets.
New conformal prediction methods for long-tailed classification problems.
The paper explores how machine learning models can be learnable despite label shifts.
Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional non-related data, how to make it effective over attributed graphs remains an open research question. E…
Manually labelling large collections of text data is a time-consuming, expensive, and laborious task, but one that is necessary to support machine learning based on text datasets. Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled …
Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structur…