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.
Introduces LDM to estimate machine learning algorithm capacity.
Proposes a method to identify elements in a skewness matrix for multivariate skew-elliptical distributions.
Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-…
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and nega…
New method improves robustness of deep learning with noisy labels.
Improved diffusion map enhances manifold regularization for semi-supervised learning.
MPVAE learns latent embeddings and label correlations for multi-label classification.
Improved graph-based semi-supervised learning with model change active learning.
GS-BSE improves label shift estimation by smoothing priors on a graph.
Ridge regression shows different behaviors in binary classification with noisy labels.
This paper identifies and estimates the label noise transition matrix without ground truth labels.
Unified view of label shift estimation methods.
Study tackles distribution shift in combinatorial settings using matrix completion techniques.
The paper analyzes matrix completion with unlabeled implicit feedback and provides error bounds.
Dual-T method improves transition matrix estimation in noisy label learning.
In this work we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting label pairwise ensembles. The main step of the correction procedure is to compute classifier-specific c…
A bidirectional loss function improves label distribution learning and enhancement.
Method estimates noise transition matrix from noisy labels without relying on unreliable class-posterior estimation.
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y$ as $[\X; \Y]$ and applied a matrix completion (MC) method to fill the missing entries, under the assumption that $[\X; \Y]$ is of low-rank…
MANO normalizes logits to estimate test accuracy without labels.
Machine learning techniques for Recommendation System (RS) and Classification has become a prime focus of research to tackle the problem of information overload. RS are software tools that aim at making informed decisions about the services that a user may like. On the other hand, classification technique deals with th…
The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground trut…
In label-noise learning, \textit{noise transition matrix}, denoting the probabilities that clean labels flip into noisy labels, plays a central role in building \textit{statistically consistent classifiers}. Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i.e…
M4L-JMF tackles multi-typed objects learning, improving on M3L.
Paper tackles noisy similarity labels for multi-class classification.
We consider the problem of estimating how well a model class is capable of fitting a distribution of labeled data. We show that it is often possible to accurately estimate this "learnability" even when given an amount of data that is too small to reliably learn any accurate model. Our first result applies to the settin…
A new method handles label noise by leveraging causal information.
Develops NPMC method for noisy labels, improving multiclass classification accuracy.
Paper tackles unsupervised learning under latent label shift across domains.
Nearest Neighbors (NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we pro…
This work efficiently learns linear threshold functions from label proportions using Gaussian distributions.
Paper proposes an alternative to anchor points for learning with noisy labels.
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we t…
The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as for multi-label learning tasks, the supervision part of GCN simply minimizes the cross-entropy loss …
Study learning and refutation in non-interactive LDP, showing sample complexity equivalence.
In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label …
Algorithm constructs prediction sets with PAC guarantees in label shift settings.
Identifies latent actions and dynamics from offline data with diverse demonstrators.
The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework. Although the methods were successfully applied under the multi-label classification framework, they have not been compared directly thus far. Such comparison is of vital importanc…
A new method improves graph-based semi-supervised classification by removing noise and mixed signs.
We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels. We formulate this problem as a non-trivial special case of one-bit rank-one matrix sensing and develop an efficient non-convex algorithm based on alternating power iteration. …
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this pap…
Graph attention network improves MLTC by capturing label dependencies.
Proposes a transductive matrix completion method with calibration for multi-task learning.
Study shows SQ hardness for multiclass linear classification with random noise.
The task of reconstructing a matrix given a sample of observedentries is known as the matrix completion problem. It arises ina wide range of problems, including recommender systems, collaborativefiltering, dimensionality reduction, image processing, quantum physics or multi-class classificationto name a few. Most works…
The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an unknown real-valued low-rank matrix from a random sample of its entries. Here, we investigate the case of highly quantized observations when …
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve…