Paper tackles online adaptation to changing label distributions.
problem Adapting machine learning models to changing label distributions in real-world settings.
method Leverages novel analysis to show estimation of expected test loss is possible without true labels. Proposes adaptation algorithms inspired by classical online learning techniques.
result Empirically verified that OGD is particularly effective and robust to various label shift scenarios.
In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated …
Enhances labels from unlabeled data using sample correlations.
problem Lack of label distributions in real-world applications.
method Proposes LESC and gLESC methods to enhance label distributions.
result Improves performance of label enhancement through sample correlations.
Paper tackles multi-label learning by improving SVR for positive semidefinite metrics.
problem Learning positive semidefinite metrics for multi-label and label distribution learning.
method Proposes two methods to overcome SVR's limitation in learning positive semidefinite metrics.
result Demonstrates new methods achieve favorable performance in multi-label and label distribution learning.
Enhances LDL by integrating distance and directional information for more robust label feature representation.
problem Lack of robust label feature representation in LDL tasks, especially with label ambiguity.
method Introduces Structural Anchor Points (SAPs) to capture inter-cluster interactions and a novel LSFs construction strategy, LIFT-SAP.
result Improves LDL performance by 15% on average across 15 real-world datasets.
Unified approach to non-standard classification tasks.
problem Non-standard classification tasks like semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning.
method Probabilistic, unified approach training a classifier to predict label-distributions, then inferring class-distributions.
result Unified model for various non-standard classification tasks.
A bidirectional loss function improves label distribution learning and enhancement.
problem Challenges in label distribution learning and label enhancement.
method Bidirectional loss function to address dimensional gap and label enhancement.
result The bidirectional loss function improves the accuracy of label distribution learning and enhancement.
Solves biased pseudo-labels in imbalanced SSL by refining them.
problem Imbalanced class distributions in semi-supervised learning lead to biased pseudo-labels.
method Formulates a convex optimization problem to refine pseudo-labels and develops an efficient algorithm, DARP.
result Demonstrates the effectiveness of DARP in various imbalanced semi-supervised scenarios.
Method converts age labels into distributions to improve speaker age estimation.
problem Label ambiguity in age labels makes precise speaker age estimation challenging.
method Converts age labels into label distributions and uses label distribution learning.
result Our method outperforms baseline methods by reducing MAE by 10% on a real-world dataset.
Bayes classifier cannot be learned from noisy labels without knowing noise distribution.
problem Learning a Bayes classifier from noisy labels when the noise distribution is unknown.
method Demonstrates the identifiability issues and proposes a simple algorithm for learning the Bayes decision rule.
result The Bayes decision rule is generally unidentified and cannot be learned without knowing the noise distribution.
Improves SSL with doubly robust estimation of unlabeled class distribution.
problem Limited labeled data and long-tailed class distributions in unlabeled data.
method Explicitly estimate unlabeled class distribution using doubly robust estimator.
result Improves performance of SSL methods on unlabeled data.
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain. To reduce the distribution shift between source and target domains, recent methods have focused on exploring invariant representations that have similar distributions across domains. However, w…
Soft labeling impacts OOD detection in neural networks.
problem Impact of soft labeling on OOD detection in deep neural networks.
method Empirical analysis of how soft labeling affects OOD detection performance.
result Soft labeling can either improve or deteriorate OOD detection performance.
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…
A new PLL method uses class activation values to improve robustness.
problem Weakly supervised learning with noisy data and adversarial perturbations.
method Subjective logic with class activation values for uncertainty representation and label weight re-distribution.
result More robust predictions under high noise levels, out-of-distribution examples, and adversarial perturbations.
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increa…
Generates samples conditioned on labels using optimal transport.
problem Estimating conditional distributions for specific labels.
method Wasserstein geodesic generator based on optimal transport theory.
result Learned conditional distributions and optimal transport maps.
New approach improves domain adaptation with label shift assumptions.
problem Improving domain adaptation when label distributions differ between source and target domains.
method Proposes generalized label shift (GLS) and modifies three DA algorithms (JAN, DANN, CDAN) to handle label distribution mismatches. result Modified DA algorithms outperform base versions, especially with large label distribution mismatches.
Estimates calibration error under label shift without labels.
problem Ensuring model reliability in the face of dataset shift without access to labels.
method Importance re-weighting of the labeled source distribution to estimate calibration error under label shift.
result Effective and reliable CE estimation with respect to the shifted target distribution.
Interpolating label noise makes models vulnerable to adversarial attacks.
problem Adversarial vulnerability of models trained on noisy labels.
method Theoretical analysis of label noise and adversarial risk relationship.
result Uniform label noise induces adversarial risk similar to worst-case poisoning.
In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch betwee…
This research tackles imbalanced continual learning with a new sampling strategy.
problem Long-tailed distribution in multi-label datasets.
method Partitioning Reservoir Sampling (PRS) for balanced knowledge of head and tail classes.
result The proposed PRS strategy maintains a balanced knowledge of both head and tail classes.
Pairwise Label Smoothing improves deep model generalization by reducing overconfidence.
problem Improving deep model generalization through regularization.
method PLS smooths labels for pairs of samples, learning distribution mass during training.
result PLS significantly outperforms LS and baseline models, reducing up to 30% classification error.
Study shows flipping a small subset of labels can severely damage machine learning models.
problem Adversarial attacks on distributed machine learning models.
method Formalized label flipping attacks, proposed a greedy algorithm, demonstrated with logistic regression models.
result A budget of only 0.1% of labels at each training step can reduce model accuracy by 6%, and some models can perform worse than random guessing when up to 25% of labels are flipped.
New method pools labels from similar data items to improve learning from small samples.
problem Learning from small, human-annotated samples with potential disagreement among annotators.
method Proposes neighborhood-based pooling for sharing labels across similar data items.
result Improves learning from small, noisy samples by pooling labels from similar items.
Detects harmful shifts without labels for model performance.
problem Detecting distribution shifts without access to labels.
method Uses a proxy derived from predictions of an error estimator.
result High power and false alarm control under various shifts.
Paper proposes SJS model to estimate model performance under covariate and label shifts.
problem Estimating model performance when both covariates and labels shift.
method Sparse Joint Shift (SJS) model and SEES algorithm.
result SEES achieves significant shift estimation error improvements over existing approaches.
Curriculum Labeling improves semi-supervised learning with pseudo-labeling, achieving high accuracy with minimal labeled data.
problem Improving semi-supervised learning with limited labeled data.
method Applying curriculum learning principles and restarting model parameters before each self-training cycle.
result 94.91% accuracy on CIFAR-10 with only 4,000 labeled samples.
New framework optimizes label shift adaptation using aligned distribution mixture.
problem Label shift where source and target label distributions differ.
method Aligned Distribution Mixture (ADM) framework, incorporating insights from generalization theory.
result The ADM framework improves four typical label shift methods and introduces a one-step approach.
Paper tackles long-tailed labels in classification problems.
problem Imbalanced or long-tailed label distribution in real-world classification problems.
method Logit adjustment applied post-hoc or during training to encourage a large relative margin between rare and dominant labels.
result Unified and generalised techniques for coping with long-tailed labels, improving generalisation and performance.
Paper quantifies label shift with robustness guarantees using distribution feature matching.
problem Estimating target label distribution under label shift.
method Distribution feature matching (DFM) framework and robustness analysis.
result General performance bound and robustness analysis in misspecified settings.
AUC-spec optimizes graph-based SSL for complex label distributions.
problem Training accurate models with scarce labeled data and abundant unlabeled data.
method Computes a low-dimensional representation that maximizes class separation via AUC optimization.
result AUC-spec achieves competitive results on synthetic and real-world datasets.
The study analyzes how label noise affects deep learning feature learning.
problem The impact of label noise on deep learning feature learning.
method Theoretical analysis of a two-layer convolutional neural network under noisy label conditions.
result Two key stages identified: signal learning in Stage I and noise memorization in Stage II.
CAN learns conditional and interventional distributions from unlabeled data.
problem Learning conditional and interventional distributions from unlabeled data.
method CAN framework with LGN and CIGN architectures, equipped with an intervention mechanism.
result CAN generates both interventional and conditional samples without needing the causal graph.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
Proposes a probabilistic approach to semi-supervised learning using normalizing flows.
problem Leveraging unlabelled data for semi-supervised learning with limited labelled data.
method Uses a normalizing flow to learn the posterior distribution over predictions for labelled data, serving as a prior for unlabelled data.
result Demonstrates improved performance on various tasks with varying output complexity.
We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise, and demonstrate its effectiveness by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider both the malicious noise model and the adversarial label nois…
A semi-supervised learning method using predefined class centroids for image classification.
problem Reducing the need for labeled data in deep learning.
method Use a small number of labeled samples and data augmentation on unlabeled samples. Constrain all samples to predefined evenly-distributed class centroids (PEDCC) using loss functions.
result Achieves state-of-the-art results with minimal labeled data.
Paper tackles dynamic label shift in online learning, achieving optimal performance.
problem Adapting to changing class marginals in online supervised and unsupervised learning.
method Develops novel algorithms reducing adaptation to online regression, achieving optimal dynamic regret.
result Achieves superior performance in various online label shift scenarios.
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.
We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal grap…
Paper improves image classification accuracy with a new Noise Modeling Network.
problem Improving performance of multi-label image classifiers with noisy or missing labels.
method Integrates a Noise Modeling Network (NMN) with a CNN to jointly learn noise distribution and CNN parameters.
result Consistently improves classification performance on MSR-COCO and MSR-VTT datasets.
EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.
problem Training a target model with no labeled data in the absence of target data labels.
method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.
result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data. However, it is often costly to have large-scale credible labels in real-world applications. One solution is to make supervised learning robust with imperfectl…
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
problem Distribution shifts in tabular data threaten model performance.
method Shift-aware uncertainty calibrator and label distribution handler.
result Up to 16% improvement on HELOC dataset.
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.
No regularization needed for InLDL, achieving efficient and effective model.
problem InLDL struggles with performance degradation due to missing degrees.
method Proposes a model that uses label distribution as a prior, implicitly regularizing the learning process.
result Achieves competitive performance without explicit regularization.