arXiv research

Bayesian networks are simplified for categorical variables using staged trees and asymmetry-labeled DAGs.

We propose a streaming algorithm for the binary classification of data based on crowdsourcing. The algorithm learns the competence of each labeller by comparing her labels to those of other labellers on the same tasks and uses this information to minimize the prediction error rate on each task. We provide performance g…

The paper cleans label noise in supervised classification using Bernoulli sampling.

NURD improves model performance by distilling representations independent of nuisance variables.

New method improves LLM judge accuracy by accounting for dependencies in aggregated binary labels.

Tests for classifier independence without ground truth labels.

SGD handles label noise with bounds improving over SGLD.

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manner…

In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they a…

CORES2 removes noisy labels by sieving out corrupted examples.

New algorithms learn and interpret asymmetry-labeled DAGs for COVID-19 fear.

SFB uses stable features to adapt unstable ones for better performance.

New framework learns disentangled causal representations from observed labels.

Active local learning uses fewer labels to predict near-optimal functions.

We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models. This loss-function encourages learning models where the distribution of the residuals between the label and the model predictio…

A supervised learning framework has been proposed for the situation where each training data is provided with a complementary label that represents a class to which the pattern does not belong. In the existing literature, complementary-label learning has been studied independently from ordinary-label learning, which as…

LFD method improves text classification by making features clearer and less label-leaking.

The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, the label noises have been treated as statistical outliers, and approaches such as importance re-weighting and bootstrap have been proposed …

Theory explains how AI models can predict unseen tasks without labeled data.

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed t…

Self-training avoids spurious features in domain adaptation.

To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version space. However, the effectiveness of volume-splitting critically depends on the initial hypothesis and this problem is also known as target…

A graph neural network transforms features in each vertex's neighborhood into a vector representation of the vertex. Afterward, each vertex's representation is used independently for predicting its label. This standard pipeline implicitly assumes that vertex labels are conditionally independent given their neighborhood…

Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this paper we propose a novel approach, Nearest Labelset using Double Distances (NLDD)…

Proposes a progressive label correction method for feature-dependent label noise.

In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlation…

A new framework CL embeds features and labels for multi-label classification.

Face recognition system trained with noisy labels.

New methods identify concepts in trained embeddings reliably without human labels.

A new method for batch prediction sets in classification problems.

Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability $ε$, together with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a set of labels, typically containing $\hat{y}$, that also contains $y$ with probability $1-ε$. Con…

The study examines the generalization of Macro-AUC in multi-label learning, identifying label imbalance as a critical factor.

The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontol…

Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: (i) Using a tighter char…

The paper studies how to use AI-generated labels in econometrics to avoid bias.

Study efficient learning of robust halfspaces with noise.

We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier …

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions. Invar…

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than i…

In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability $ρ\in[0,0.5)$, and the random label noise can be class-co…

The study analyzes how label noise affects deep learning feature learning.

New method uses weak labels to create valid confidence sets for predictions.

FreDF improves forecasting by learning in the frequency domain.

New bound proves rationality helps generalization in self-supervised learning.

Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RK…

Unified surrogate loss framework for multi-label learning with strong consistency guarantees.

This paper examines error bounds for deep learning classifiers with noisy labels.

A method to approximate instance-dependent label noise using instance-confidence embedding.