FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
ASEs use surrogate estimation to efficiently evaluate model performance with minimal labels.
problem Efficient model evaluation with limited labels.
method Surrogate-based estimation and active learning.
result ASEs offer greater label-efficiency than current methods for deep neural networks.
Novel framework for efficient entity alignment labeling.
problem Efficient labeling of entity alignments in knowledge graphs.
method Active and passive learning strategies to select informative instances.
result Passive learning achieves comparable performance to active learning.
Efficiently learns sparse halfspaces with noisy labels.
problem Learning sparse halfspaces with arbitrary bounded noise.
method Polynomial time algorithm for active learning with improved label complexity.
result First efficient algorithm with label complexity polynomial in 1 1 − 2 η \frac{1}{1-2η} 1 − 2 η 1 . Efficiently learns sparse halfspaces with minimal label queries.
problem PAC active learning of sparse linear classifiers.
method Attribute-efficient algorithm with sublinear label complexity.
result Achieves label complexity of O(t · polylog(d, 1/ε)) under certain assumptions.
Efficient algorithms learn from coarse labels instead of fine grained ones.
problem Learning from coarse labels when fine labels are unavailable.
method Formalized coarse label settings, used a reduction for SQs, and provided efficient algorithms.
result Any problem learnable from fine labels can be learned efficiently from coarse labels.
Efficient graph-based algorithm for learning from bagged data.
problem Learning from bagged data with label proportions.
method Graph-based algorithm encouraging local smoothness and exploiting global structure.
result Preserves the mass of each bag while recovering true labels.
ELSA efficiently adapts to label shift without post-prediction calibrations.
problem Domain adaptation with label shift across training and testing datasets.
method Moment-matching framework based on influence function geometry; solves linear systems for adaptation weights.
result ELSA estimator is n \sqrt{n} n -consistent and asymptotically normal, achieving state-of-the-art estimation performance. New algorithms reduce label collection for online prediction with expert advice.
problem Efficiently predicting binary sequences with expert advice using fewer labels.
method Adaptive selective sampling for exponentially weighted forecasters.
result Label complexity scales roughly as the square root of the number of rounds for a scenario with a strictly better expert.
Efficient Perceptron learns halfspaces with minimal labels under noise.
problem Efficiently learning halfspaces with few labels in noisy conditions.
method Active Perceptron algorithm for homogeneous halfspaces under uniform distribution.
result Near-optimal label complexities and time complexities for both noise conditions.
Efficiently discovers multi-label rules with relaxed pruning.
problem Learning multi-label heads in multi-label classification.
method Relaxed pruning approach to induce more expressive rules.
result Relaxed pruning leads to more expressive rules without sacrificing performance.
Efficiently tests two distributions with few label queries.
problem Two-sample test with limited label information.
method Three-stage framework: classifier training, bimodal query, FR test.
result Significantly reduces Type II error compared to uniform querying.
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.
Efficiently learns from partial labels using variational inference.
problem Learning from noisy and ambiguous partial labels in crowdsourcing.
method Amortized variational inference for probabilistic posterior approximation.
result Achieves state-of-the-art performance in accuracy and efficiency.
Proposes candidate labeling for more efficient use of crowdsourced labels.
problem Inefficiency of standard crowd learning where annotators provide single labels.
method Allows annotators to provide multiple labels per instance.
result Candidate labeling extracts more knowledge from labelers than standard approach.
Efficiently learns representations across domains and tasks with few labels.
problem Learning representations that generalize across different domains and tasks with limited labeled data.
method Combines domain adversarial loss and metric learning for representation transfer. Optimizes on both labeled and unlabeled data in the target domain.
result Significantly outperforms fine-tuning on novel classes in new domains with few labeled examples.
Paper introduces active Bayesian method for assessing black-box classifiers efficiently.
problem Need to assess performance of black-box classifiers reliably with limited labels.
method Develops inference strategies and proposes active Bayesian framework for efficient instance selection.
result Significant gains in performance assessment with fewer labels compared to traditional methods.
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.
Proposes a self-paced multi-label learning method to handle diverse labels efficiently.
problem Learning from multi-label data with a large label space is NP-hard and prone to overfitting.
method Self-paced multi-label learning with diversity (SPMLD) approach, incorporating gradual label inclusion and diversity maintenance.
result The proposed SPMLD framework optimizes a non-convex objective function using block coordinate descent.
Efficiently learns halfspaces with malicious noise, near-optimal label complexity.
problem Learning s s s -sparse halfspaces under malicious label noise. method Active learning algorithm with instance reweighting and empirical risk minimization.
result Near-optimal label complexity of O ( s log 4 d / ε ) O(s \log^4 d / ε) O ( s log 4 d / ε ) and noise tolerance Ω ( ε ) Ω(ε) Ω ( ε ) . Optimal online learning algorithms for label-efficient prediction and bandits.
problem Efficient prediction in online learning with limited information.
method Optimistic online mirror descent with second order corrections and hybrid regularizers.
result Improved regret bounds for label-efficient prediction and bandits.
An efficient algorithm identifies labels from sparse pooled data.
problem Identify unknown labels from condensed measurements of items.
method Design an efficient algorithm based on novel pooling scheme.
result Resolves statistical-to-computational gap in minimum pools needed.
Graph filtering framework improves semi-supervised learning efficiency.
problem Efficiently leverage unlabeled data with limited labeled data.
method Graph filtering to inject graph similarity into data features.
result Unified insights and improved modeling capabilities of label propagation and graph convolutional networks.
Study efficient learning of robust halfspaces with noise.
problem Learning robust halfspaces in the presence of adversarial perturbations and random label noise.
method Provides conditions for robust learnability and a simple algorithm for any ℓ_p perturbation.
result Simple computationally efficient algorithm for robust learning with random label noise.
Dynamic classifier chains improve multi-label classification efficiency.
problem Building efficient multi-label classification models.
method Dynamic ensemble of chain classifiers using Naive Bayes and nearest neighbor approaches, with heuristic for label order optimization.
result The proposed dynamic chain model based on Naive Bayes classifier and heuristic is efficient for multi-label classification.
This work analyzes label embedding for large multiclass classification problems.
problem Label embedding for large multiclass classification problems.
method Analysis of label embedding in extreme multiclass classification, presenting an excess risk bound and showing a trade-off between computational and statistical efficiency.
result The statistical penalty for label embedding vanishes with sufficiently low coherence under the Massart noise condition.
ETM models improve efficiency in semi-supervised logistic regression.
problem Improving efficiency in logistic regression with limited labeled data.
method Developed exponential tilt mixture (ETM) models for semi-supervised estimation.
result ETM-based estimation demonstrates improved efficiency over supervised logistic regression.
A deep learning method for XML with autoencoder and ranking loss.
problem XML with large label collections, high complexity, inter-label and feature dependencies, and noisy labels.
method Word-vector-based self-attention, ranking-based AutoEncoder architecture.
result Competitive performance on benchmark datasets.
Improved audio classification with limited labels using multitask and self-supervised learning.
problem Limited labeled data for audio classification.
method Multitask learning and self-supervised learning on unlabeled data.
result Significant improvement in performance (up to 6%) through multitask and self-supervised learning.
A new learning scheme improves model efficiency and performance.
problem Characterizing correlation between batch-level and global data distributions.
method Epoch-evolving Gaussian Process Guided Learning (GPGL) scheme with context labels and triangle consistency loss.
result Significantly outperforms existing models on mainstream datasets.
A non-convex algorithm recovers low-rank matrices from one-bit labels efficiently.
problem Learning with one-bit labels in multi-label scenarios.
method Formulated as one-bit rank-one matrix sensing, developed an alternating power iteration algorithm.
result Achieves linear convergence and nearly optimal sampling complexity.
This paper tackles label-efficient evaluation in extreme class imbalance.
problem Challenges in obtaining a sufficient sample for accurate evaluation in tasks with extreme class imbalance.
method Develops a framework for online evaluation based on adaptive importance sampling.
result Establishes strong consistency and a central limit theorem for performance estimates.
Efficient active learning with abstention reduces label complexity exponentially.
problem Achieving high accuracy with minimal labels.
method Developed a computationally efficient active learning algorithm with abstention.
result Achieves polylog(1/ε) label complexity, reducing by an exponential factor.
PDO optimizes LLM prompts without labels, improving performance.
problem Optimizing prompts for LLMs without access to labeled data.
method Pairwise preference feedback, dueling bandits, Thompson Sampling, mutation.
result PDO identifies stronger prompts than label-free methods.
This work efficiently learns linear threshold functions from label proportions using Gaussian distributions.
problem Efficiently learning linear threshold functions from label proportions.
method Using Gaussian distributions, the algorithm estimates means and covariance matrices, and identifies a low error hypothesis LTF.
result It is possible to efficiently learn LTFs using LTFs when given access to random bags of label proportions.
MEC improves efficiency and robustness in semi-supervised inference.
problem Efficient inference with limited labeled data and robust uncertainty quantification.
method Machine-Learning-Assisted Generalized Entropy Calibration (MEC) using cross-fitted, calibration-weighted PPI.
result MEC achieves semiparametric efficiency bounds under weaker assumptions and provides near-nominal coverage.
Efficient surrogate losses and regularization methods for structured prediction.
problem Efficiency and performance in structured prediction with rich label structures.
method Development of bi-criteria surrogate losses and shared Frobenius norm for regularization.
result Improved efficiency and performance in inference and optimization for structured prediction.
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.
The paper proposes a new dictionary learning method for faster and more accurate image classification.
problem Efficient and accurate image classification with compact dictionaries.
method Cross-label suppression and group regularization to learn a discriminative dictionary.
result The proposed method achieves better classification accuracy and computational efficiency compared to existing methods.
Active inference framework improves U U U -statistic estimation efficiency.
problem Costly acquisition of labels for U U U -statistics. method Active inference framework with optimal sampling rule.
result Substantial gains in estimation efficiency over baseline methods.
The paper explores learning from label proportions, showing differences in efficiency between LLP and PAC learning.
problem Learning from label proportions (LLP) in unlabeled data with given label proportions.
method Formal definition and computational complexity analysis of LLP learning.
result LLP learning is more restrictive than PAC learning for finite VC classes, and some classes are uncharacterizable.
Efficient algorithm for halfspaces with specific noise conditions.
problem Learning halfspaces with Massart and Tsybakov noise.
method PAC active learning algorithm for d-dimensional halfspaces.
result Near-optimal label complexity under Massart noise and lower than passive learning under Tsybakov noise.
Efficiently selects nearest neighbors for labeling to speed up active learning.
problem Intractable active learning and search for large-scale unlabeled data.
method Restricts candidate pool to nearest neighbors of labeled set.
result Achieved similar performance to global approach but reduced computational cost by up to 3 orders of magnitude.
Improved active learning for counterfactual learning from observational data.
problem Learning a classifier from observational data with selection bias.
method Active learning with a counterfactual risk minimizer, modifying both risk and active learning process.
result Statistically consistent and more label-efficient algorithm compared to prior work.
Paper tackles inherent risk scoring with choice-based data labeling and synthetic data collection.
problem Inconsistent expert judgments and lack of labeled data in inherent risk scoring.
method Choice-based data labeling and synthetic data collection.
result System achieves 89% accuracy on a test set of 52 examples.
A new method embeds labels and group information for efficient multi-label classification.
problem Efficient multi-label classification with label sparsity and group structure.
method Identifies label groups, embeds labels and features in a low-dimensional space preserving sparsity and group structure.
result Our method outperforms state-of-the-art algorithms on benchmark datasets.
Proposes JBNN for multi-label classification with improved efficiency and performance.
problem Multi-label classification with dependencies and heavy computational load.
method Joint Binary Neural Network (JBNN) that synchronously performs multiple binary classifications and captures label relations via joint binary cross entropy (JBCE) loss.
result Significantly better performance and computational efficiency compared to state-of-the-art methods.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.