Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…
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48 results for “Labeling Cost”
MCAL reduces labeling costs by 6x for auto-labeling data sets.
problem Expensive human annotation for ground-truth data sets.
method Iterative approach that trains a classifier to auto-label part of the data set, determining which samples to label using humans and which to label using the classifier at each step.
result 6x lower overall cost compared to human labeling the entire data set, always cheaper than competing strategies.
We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data t…
Method curates cost-effective, high-quality datasets using AI models.
problem Costly manual labeling of datasets.
method Probably Approximately Correct Labels (PACL) method.
result Curates high-quality datasets with low overall labeling error.
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms whe…
Label tree-based algorithms are widely used to tackle multi-class and multi-label problems with a large number of labels. We focus on a particular subclass of these algorithms that use probabilistic classifiers in the tree nodes. Examples of such algorithms are hierarchical softmax (HSM), designed for multi-class class…
Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.
problem Static label ordering in multi-label classification limits model performance.
method Combining dynamic classifier chains with XGBoost for efficient multi-label prediction.
result Dynamic label ordering improves model performance and reduces training costs.
FavMac maximizes value while controlling cost in multi-label prediction.
problem Value-maximizing predictions with strict cost control in multi-label scenarios.
method FavMac pipeline combining any multi-label classifier with online update mechanism.
result FavMac achieves higher value with strict cost control compared to baselines.
New method reduces labeling costs in semi-supervised learning.
problem Efficiently label data with limited labeled samples.
method Formulates batch acquisition as bilevel optimization.
result Shows effectiveness in keyword detection tasks.
Cost-efficient feature selection for multi-label classification in medicine.
problem Feature selection in multi-label classification with cost constraints.
method Sequential feature selection maximizing conditional mutual information, followed by cost-free feature selection using shadow features.
result The method effectively reduces prediction costs in medical applications.
An active learner chooses which data points to label to minimize cost and error in streaming data.
problem Efficiently labeling streaming data points with limited labeling costs.
method Formalizes the problem with a loss function, designs an algorithm with a time and cost dependent threshold, and provides upper and lower bounds.
result The algorithm achieves a worst-case upper bound of on the loss after rounds.
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
Semi-supervised K-means++stat.ML
Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights ({\tt k-means++}) has recently been used to improve the performance over this strategy in cost and run-time. We consider the k-means problem with semi-supervised inf…
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Active testing reduces label costs for efficient model evaluation.
problem Real-world applications require expensive test labels, disconnecting from existing model evaluation methods.
method Derives acquisition strategies to select test points efficiently, addressing label bias and variance.
result Active testing improves model evaluation efficiency without sacrificing accuracy.
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.
A method to generate multi-label data from single positive annotations.
problem Generating multi-label datasets is costly and impractical.
method Single-to-multi-label (S2M) sampling using Markov chain Monte Carlo.
result S2M sampling enables high-quality multi-label data with minimal annotation cost.
Domain Adaptation in 6G wireless networks: When is it green?
problem Energy consumption of Domain Adaptation (UDA) compared to single-task training in 6G wireless networks.
method Investigate energy consumption and propose a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
result Proposed a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
Active learning selects samples for labeling to build accurate models with minimal labeled data.
problem Costly acquisition of labeled data in supervised learning.
method Adaptive selection of unlabeled data samples for labeling.
result Efficient model building with minimal labeled data.
CTS machines improve screen development in printing industries, reducing costs and increasing profitability.
problem Inefficient screen development process in heat transfer printing and labeling.
method Implementation of CTS (computer-to-screen) technology for screen development.
result Reduction in material and operational costs, leading to profitability.
AM-PPI uses multiple predictors to reduce label cost in healthcare AI.
problem Reduces label cost in post-deployment monitoring of healthcare AI.
method Combines model predictions with a small labeled sample, routing each instance to a cost-appropriate subset of predictors.
result Produces narrower confidence intervals than single-predictor methods.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classificationcs.LG
Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning…
A new active learning method considers both uncertainty and diversity to minimize labeling and decision costs.
problem Classical AL approaches fail to capture data distribution in unlabeled data, leading to mislabeling of outliers.
method CBAL considers classification uncertainty and instance diversity, using a min-max approach to minimize labeling and decision costs.
result Extensive experiments show CBAL outperforms state-of-the-art AL approaches.
Neural processes approximate Gaussian process inference, revealing three key costs.
problem Approximating Gaussian process inference with neural processes.
method Bounding KL divergence into three components: label contamination, information bottleneck, and amortization error.
result Characterization of three costs of amortizing Gaussian process inference with neural processes.
Budgeted deferral framework reduces expert query costs in machine learning.
problem High cost of querying all experts in deferral algorithms.
method Introduces budgeted deferral framework for selective expert querying.
result Empirical results show substantial reduction in training costs without sacrificing accuracy.
CAMS selects best pre-trained model for unlabeled data points.
problem Efficiently utilizing pre-trained models and unlabeled data.
method Contextual active model selection algorithm with two components: contextual model selection and active query.
result CAMS requires less than 10% labeling effort compared to existing methods, achieving similar or better accuracy.
Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the mo…
Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.
problem Effects of label set heterogeneity and privacy constraints in federated learning.
method Apply classical classifier combination methods and adapt FL methods for private label sets, compare public and private settings.
result Reducing labels harms model performance, but centralized tuning can help.
Active Coveringcs.LG
Active learning method reduces label queries for positive examples.
problem Minimizing label queries for identifying all positive examples.
method Repurposed support estimator as offline algorithm, and a simple active learning method.
result Improved excess query cost of compared to offline methods.
Improved method for encoding contingency tables reduces mutual information bias.
problem Mutual information bias in measuring label similarity.
method Improved method for encoding contingency tables to reduce information cost.
result Better bound on reduced mutual information in typical use cases.
Unified method for learning from selectively labeled data.
problem Classification with selectively labeled data from multiple decision-makers.
method Unified cost-sensitive learning (UCL) approach.
result Unified method for robust classification in selective labeling.
Small LLMs outperform large ones on simple tasks without extra labelling costs.
problem Performance of large commercial models in simple classification tasks.
method Logistic Regression on small LLM embeddings.
result Small LLMs equal or outperform large LLMs in 'tens-of-shot' classification tasks.
Study cost-effective fairness audits with partial feedback, improving over random exploration.
problem Auditing fairness of classifiers with limited true labels.
method Introduces cost model, proposes near-optimal algorithms for black-box and mixture models.
result Significantly lower audit costs compared to natural baselines.
New method quantifies uncertainty at class level for better decision-making.
problem Improving cost-sensitive decision-making in classification tasks.
method Label-wise decomposition of uncertainty measures based on non-categorical metrics.
result Proposed measures adhere to desirable properties and improve uncertainty quantification.
Deep convolutional neural networks have achieved great success in various applications. However, training an effective DNN model for a specific task is rather challenging because it requires a prior knowledge or experience to design the network architecture, repeated trial-and-error process to tune the parameters, and …
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that - Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; un…
We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assessed with respect to a surrogate loss, to its cost-sensitive classification risk. Two approaches to surrogate regret bounds are developed. The…
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimizationcs.LG
CADO optimizes heatmap-based solvers for cost minimization, overcoming performance limitations.
problem Heatmap-based solvers lack objective alignment for cost minimization.
method CADO uses Reinforcement Learning to optimize solution cost directly, introducing Label-Centered Reward and Hybrid Fine-Tuning.
result CADO achieves state-of-the-art performance across diverse benchmarks.
A cost-effective approach to label acquisition using active learning markets.
problem Improving model fitting and training for predictive analytics.
method Formalizing market clearing as an optimisation problem, integrating budget constraints and improvement thresholds, using two active learning strategies with distinct pricing mechanisms.
result Superior performance with fewer labels acquired compared to conventional methods.
There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to…
Paper proposes a cost-sensitive conformal training method with provably controllable learning bounds.
problem Uncertainty quantification and learning bounds in conformal prediction.
method Cost-sensitive conformal training algorithm that minimizes the expected size of prediction sets using rank weighting.
result Theoretical analysis shows tightness between weighted objective and expected size of conformal prediction sets.
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…
New method reduces labeler costs by aggregating predictions from local classifiers.
problem Reduce labeler costs in multiclass classification.
method Model -class classification using smaller classifiers trained on subsets of tasks.
result Near-optimal scheme for designing classifier configurations reduces labeler costs.
The development of real-time affect detection models often depends upon obtaining annotated data for supervised learning by employing human experts to label the student data. One open question in annotating affective data for affect detection is whether the labelers (i.e., human experts) need to be socio-culturally sim…
A new ensemble of Gaussian processes improves active learning efficiency.
problem Efficiently labeling data in high-cost domains like medical imaging.
method Adaptive weighted ensemble of Gaussian processes (EGP) for active learning.
result EGP-based approaches outperform single GP-based active learning methods.
Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. Our scheme, bounded expectation of label assignment (BELA), greedily queries an oracle (or human labeler…
Retraining with predicted labels improves model accuracy in noisy settings.
problem Improving model accuracy with noisy or corrupted labels.
method Retraining with predicted hard labels in a linearly separable binary classification setting.
result Retraining with predicted labels can increase model accuracy, as proven theoretically.