Adaptive sampling detects local concept drift with limited labels.
problem Detecting local concept drift in dynamic environments with scarce labels.
method Combines residual-based exploration and exploitation with EWMA monitoring.
result Superior performance in label efficiency and drift detection accuracy.
Paper tackles representation learning with limited labels from crowdsourced data.
problem Limited labeled data and inconsistent crowdsourced labels.
method Grouping-based deep neural network and Bayesian confidence estimator.
result Framework learns effective representations from limited data with crowdsourced labels.
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer…
New research shows label refinement and weak training have limitations for aligning LLMs.
problem Limitations of refinement methods for aligning large language models.
method Analyzed probabilistic assumptions and alternative approaches to label refinement and weak training.
result Label refinement and weak training suffer from irreducible error, leaving a performance gap.
Study shows algorithms benefit from limited target data with many source domains.
problem Adapting to new domains with scarce labeled target data.
method New family of model selection algorithms.
result Beneficial guarantees in scenarios with limited target data.
Proposes a new contrastive loss for semi-supervised medical image segmentation.
problem Lack of labeled data for medical image segmentation.
method Uses pseudo-labels and a local contrastive loss to learn good local representations.
result Achieved high segmentation performance on public cardiac and prostate datasets.
Paper proposes Universum GANs to improve GANs with limited labeled data.
problem Limited labeled data makes supervised learning challenging.
method Proposes Universum GANs with evolving discriminator loss.
result Improved discriminator accuracy and high quality data generation.
Learning representation has been proven to be helpful in numerous machine learning tasks. The success of the majority of existing representation learning approaches often requires a large amount of consistent and noise-free labels. However, labels are not accessible in many real-world scenarios and they are usually ann…
New method selects data for labeling in RKHS to improve regression accuracy.
problem Labeling cost in supervised learning.
method Importance labeling scheme in RKHS with gradient descent.
result Gradient descent with proposed labeling scheme achieves optimal convergence rate.
Develops gradient boosting for multi-label classification.
problem Lack of customizable learning algorithms for multi-label classification.
method Generalizes gradient boosting to multi-output problems and proposes an algorithm for learning multi-label classification rules.
result Ability to minimize both decomposable and non-decomposable loss functions.
DynaCor detects noisy labels by learning from corrupted training signals.
problem Label noise in real-world datasets hinders model generalization.
method DynaCor introduces label corruption to indirectly simulate noisy labels and learns to distinguish clean from noisy instances.
result DynaCor outperforms state-of-the-art competitors in noisy label detection.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
BCCP uses bandit feedback to provide reliable predictions with limited labeled data.
problem Limited labeled data and bandit feedback challenge online set-valued classification.
method BCCP uses stochastic gradient descent to train model and make set-valued inferences with unbiased estimation of true label.
result BCCP offers coverage guarantees on a class-specific granularity.
Paper proposes Meta Label Learning to infer global labels for robust few-shot models.
problem Few-shot learning with limited training data.
method Meta Label Learning (MeLa) framework that infers global labels.
result MeLa framework is competitive with existing methods and robust for few-shot learning.
Efficiently classifies binary labels with XOR queries, even under noisy conditions.
problem Binary classification with unknown labels using XOR queries.
method Effective query type and an efficient inference algorithm for noisy conditions.
result Achieves information-theoretic limit on optimal number of queries.
PPI uses proxy data to improve inference from limited labels across related tasks.
problem Statistical inference with limited labels across multiple related tasks.
method Prediction-powered inference framework that uses cross-task recalibration to improve power and accuracy.
result Cross-task recalibration can substantially reduce confidence interval widths when labels are scarce.
Self-supervised method improves biosignal models with limited labeled data and subjects.
problem Limited labeled data and subjects in biosignals datasets.
method Contrastive learning with subject-aware loss and data augmentation.
result Self-supervised embeddings yield competitive results compared to supervised methods.
The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain so as to infer an accurate model in a target domain where labels are not available or in scarce at the best. A state-of-the-art approach for the DA is due to (Ganin et al. 2016), known as DANN, where they attempt to induce a…
New taxonomy reveals different detection limits for various types of fraud.
problem Existing fraud detection treats all fraud as the same, ignoring its diverse forms.
method Introduced an observation-mechanism taxonomy with five fraud classes.
result Separate estimation by fraud class outperforms pooled estimation.
Over the last couple of years, deep learning and especially convolutional neural networks have become one of the work horses of computer vision. One limiting factor for the applicability of supervised deep learning to more areas is the need for large, manually labeled datasets. In this paper we propose an easy to imple…
GraphGen generates large labeled graphs efficiently and accurately.
problem Scalability and comprehensive evaluation of graph generation techniques.
method Converts graphs to sequences using minimum DFS codes and learns joint distributions with an LSTM.
result Significantly faster and better quality than state-of-the-art techniques.
Improved diffusion map enhances manifold regularization for semi-supervised learning.
problem Limited performance of manifold regularization models in capturing global structure.
method Enhanced diffusion map with improved label propagation function.
result Proposed method improves manifold regularization model's performance.
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particul…
Active WeaSuL uses active learning to improve weak supervision for better model performance.
problem Limited labelled data in machine learning.
method Combines active learning with weak supervision to improve probabilistic labels.
result Active WeaSuL outperforms weak supervision and active learning with limited labelled data.
Innovative warping labeling for twisted knots and braids.
problem Inventing an invariant for twisted knots and braids.
method Introducing warping degree, constructing warping labeling, extending to virtual braids.
result Developed invariants for twisted knots and braids using warping labeling.
The paper establishes limits of transfer learning with neural networks.
problem Understanding the fundamental limits of transfer learning.
method Statistical minimax framework for regression with linear and neural network models.
result Lower bounds for target generalization error.
Paper tackles active learning under human label variation, proposing a new framework.
problem Active learning assumes a single ground truth, ignoring human label variation.
method Survey and propose a conceptual framework for HLV-aware active learning.
result Lay a conceptual foundation for HLV-aware active learning.
While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a challenge. The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through a combin…
Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attent…
Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but …
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…
MeLa learns task relations by inferring global labels for robust FSL.
problem Few-shot learning with limited global labels.
method Meta Label Learning (MeLa) and augmented pre-training.
result MeLa outperforms existing methods across diverse benchmarks.
Efficient method for generating adversarial examples with limited query budget.
problem Developing black-box adversarial attacks with limited information.
method Bayesian Optimization in a structured low-dimensional subspace.
result Significantly higher attack success rate with fewer queries.
New method tackles dynamic data labeling issues with limited labels.
problem Dynamic data labeling with scarce labeled instances.
method Instance exploitation technique for aggressive model adaptation.
result Aggressive model adaptation leads to better performance than standard methods.
Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start w…
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in practice: 1) only limited labeled samples are available due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noises (e.g., mi…
The paper characterizes the efficiency of transferring knowledge from a teacher to a student classifier over finite domains.
problem Characterizing the statistical efficiency of knowledge transfer over finite domains.
method Three progressive levels of privileged information: hard labels, teacher probabilities, and soft labels. Novel empirical loss functions used to achieve the fundamental limits.
result Achieving the fundamental limits of knowledge transfer through specific levels of privileged information and novel loss functions.
This work uses self-supervised learning to generate better labels for financial time-series data.
problem Lack of reliable labels for financial time-series data due to noise and non-stationarity.
method Inspired by image classification, applies computer vision techniques to financial time-series data to generate denoised labels.
result Generated denoised labels improve the performance of downstream learning algorithms.
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introduces doubly robust self-training, a method that combines labeled and pseudo-labeled data to balance between labeled-only and pseudo-labeled-only training.
result Demonstrates superior performance of doubly robust self-training on ImageNet and nuScenes datasets.
Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them hav…
IUPM monitors machine learning models under gradual shifts using optimal transport and active labeling.
problem Gradual distribution shifts lead to unnoticed accuracy declines in machine learning models.
method Incremental Uncertainty-aware Performance Monitoring (IUPM) using optimal transport and active labeling.
result IUPM outperforms existing baselines in gradual shift scenarios and guides label acquisition more effectively.
ActiveLab improves classifier accuracy with fewer annotations by re-labeling.
problem Imperfect labels from multiple annotators in real-world data.
method ActiveLab automatically decides when to re-label examples for better classifier training.
result ActiveLab trains more accurate classifiers with fewer annotations.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
A framework learns dynamic soft labels to improve model generalization and accuracy.
problem Models trained on one-hot labels overfit and are sensitive to noisy annotations.
method Proposes a framework where labels are treated as learnable parameters, adapting dynamically during optimization.
result Consistent gains across different datasets and architectures, improving ResNet18 by 2.1% on CIFAR100.
The paper explains how data augmentation improves semi-supervised learning efficiency.
problem Improving accuracy from a small fraction of labeled data.
method Data augmentation induces a similarity graph, which is graph-Laplacian-regularized for downstream learning.
result A fast transductive rate of O(1/nL) is achieved, reducing the number of labels needed. New model optimizes worker-task specialization for crowdsourcing.
problem Inferring correct labels from noisy answers across varying worker and task skills.
method Introduced a d-type specialization model to account for varying worker and task types, and proposed algorithms achieving optimal sample complexity. result Optimal label inference algorithms for crowdsourcing with unknown worker and task types.
Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases expo…