New method improves auto-labeling accuracy by optimizing confidence functions.
problem Overconfident model scores lead to poor TBAL performance.
method Developed a new post-hoc method, Colander, to optimize TBAL confidence functions.
result Achieves up to 60% improvement in coverage over baseline methods.
New attacks can infer model training membership using only label predictions, not confidence.
problem Inferring whether a data point was used to train a machine learning model.
method Evaluate model's predicted labels under perturbations to infer membership.
result Label-only attacks perform as well as confidence-based attacks and break defenses that rely on confidence masking.
Paper introduces methods for more reliable probabilistic predictions with confidence intervals.
problem Inaccurate labeling of datasets due to unreliable probabilistic predictions from weak labeling functions.
method Proposes a methodology to provide confidence intervals for label probabilities using uncertainty sets of distributions.
result Improves reliability of probabilistic predictions and provides confidence intervals for label probabilities.
Improves confidence calibration in neural networks by smoothing labels based on class similarity.
problem Improving confidence calibration in deep neural networks for safety-critical applications.
method Proposes a novel label smoothing technique where label values are based on similarities with the reference class, using different similarity measurements.
result Consistently outperforms state-of-the-art calibration techniques on various datasets and network architectures.
A method to approximate instance-dependent label noise using instance-confidence embedding.
problem Real-world label noise that depends on individual instances.
method Variational approximation with instance embedding to capture instance-specific label corruption.
result ICE method effectively approximates instance-dependent noise and detects ambiguous instances.
Exploits class similarity for better machine learning models with confidence labels and projective loss functions.
problem Poor model performance due to confusing similar classes.
method Exploits class similarity with confidence labels and projective loss functions.
result Improved model performance on noisy labels.
The paper investigates how dataset quality and heterogeneity affect model confidence in machine learning.
problem Understanding how dataset quality and heterogeneity impact model confidence in machine learning.
method The study uses theoretical explanations and experimental demonstrations to investigate the effects of dataset size, label noise, and class heterogeneity on model confidence.
result Label noise reduces model confidence, while reduced dataset size increases it, and class heterogeneity leads to inconsistent confidence across classes.
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data. A line of research stemming from semi-supervised learning uses pseudo labeling to directly g…
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
Improved AI lung ultrasound segmentation using expert confidence values.
problem Label uncertainty in lung ultrasound due to subjective interpretation by radiologists.
method Designing a data annotation protocol capturing expert confidence, training AI on binarized labels with confidence thresholds.
result Improved AI segmentation and better clinical outcomes (e.g., S/F oxygenation ratio estimation, patient readmission prediction).
Proposes top-label calibration and M2B framework for multiclass to binary calibration.
problem Multiclass calibration and interpretation issues.
method Top-label calibration and M2B reduction framework.
result M2B + HB achieves lower calibration error than other methods.
A new confidence measure improves self-training in biased data.
problem Improving self-training in biased data.
method Proposes a new confidence measure, T-similarity, based on ensemble diversity of linear classifiers.
result Empirically shows the benefit of T-similarity for pseudo-labeling policies on various datasets.
Mutual teaching improves graph models with less labeled data.
problem Training graph models with limited labeled data.
method Dual model training with mutual teaching strategy.
result Significant performance improvement with less labeled data.
SelectMix improves deep learning robustness against noisy labels.
problem Deep neural networks memorize noisy labels, degrading performance.
method Confidence-guided targeted sample mixing with soft labels.
result SelectMix consistently outperforms baseline methods on noisy label datasets.
PML-LFC improves PML by estimating label confidence from both feature and label spaces.
problem PML challenges in real-world scenarios where only some labels are relevant.
method PML-LFC estimates label confidence using feature and label space similarities, training a predictor with these values.
result PML-LFC achieves superior performance on synthetic and real-world datasets.
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…
New method uses weak labels to create valid confidence sets for predictions.
problem Lack of labeled data in machine learning models.
method Developed a conformal prediction framework to provide valid predictive confidence sets using weakly labeled data.
result New coverage definition allows for tighter and more informative (but valid) confidence sets.
The study examines when to trust confidence thresholding in pseudo-labelling regression.
problem Calibrated probabilities from classifiers used for pseudo-labelling need careful handling to avoid bias in downstream regression.
method Developed a diagnostic apparatus to predict and bound the bias induced by confidence thresholding, derived a closed-form expression for the attenuation bias.
result The bias can be predicted from the residual score variance V∗, motivating a structural separation between classifier features and downstream controls. Improved drug-protein interaction prediction using FTL method.
problem Predicting drug-protein interactions from noisy data with uncertain labels.
method Filtered Transfer Learning (FTL) method that fine-tunes a deep neural network across multiple tiers of data confidence.
result FTL method outperforms deep neural networks trained on single confidence ranges.
Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, …
Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the …
RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.
problem Mixup degrades SSL performance by compromising artificial labels purity.
method RegMixMatch integrates high- and low-confidence samples, uses class-aware Mixup, and mitigates confirmation bias.
result RegMixMatch achieves state-of-the-art performance in SSL benchmarks.
VR game data for P300 BCI with raccoon vs demon stimuli.
problem Developing confidence metrics for P300 BCI.
method Multiclass labeled P300 dataset in VR game context.
result Estimation of model's confidence in stimulus predictions.
StratPPI improves prediction-powered inference with stratified sampling.
problem Improving statistical estimates with limited human-labeled data.
method Combining small human-labeled data with large automatic-labeled data, stratifying data for tighter confidence intervals.
result StratPPI provides substantially tighter confidence intervals than unstratified approaches.
CAT improves domain adaptation for fault diagnosis by calibrating teacher network predictions.
problem Performance drops in deep learning models when applied to different data distributions.
method CAT uses post-hoc calibration techniques to calibrate predictions of the teacher network during self-training.
result CAT achieves state-of-the-art performance on most transfer tasks in domain-adaptive IFD.
The paper addresses the difficulty of decision makers trusting AI-assisted predictions and proposes a method to improve confidence values.
problem Decision makers struggle to trust AI-assisted predictions based on confidence values.
method The paper investigates why decision makers have difficulties and proposes a method to construct more useful confidence values.
result Multicalibration with respect to the decision maker's confidence on her own predictions is a sufficient condition for alignment, leading to better decisions.
Contrastive regularization improves semi-supervised learning by better propagating confident pseudo-labels.
problem Consistency regularization's limitation in high performance and efficiency.
method Proposes contrastive regularization to update model features, pushing confident labels into unlabeled samples.
result Improves semi-supervised learning tasks with fewer training iterations and robust performance.
Active inference uses machine learning to prioritize data labeling for more efficient statistical inference.
problem Efficiently collecting data points for statistical inference with limited labels.
method A machine learning-assisted approach that identifies uncertain data points for labeling.
result Achieves the same level of accuracy with fewer samples, resulting in smaller confidence intervals and more powerful p-values.
Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.
problem Lack of pointwise labels due to privacy, confidentiality, or security reasons.
method Developed Pcomp classification, derived an unbiased risk estimator (URE), and improved it using correction functions and consistency regularization.
result Demonstrated the effectiveness of Pcomp classification methods.
Calibrates network confidence for unsupervised domain adaptation.
problem Calibrating a model trained on a source domain to a target domain without labeled data.
method Estimates network accuracy on the target domain and calibrates prediction confidence directly in the target domain.
result Significantly outperforms existing methods across standard datasets.
Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field. A particularly damaging problem is that DNN models often give false predictions with high confidence, due to the unavoidable slight divergences between operation data and training data. To…
The study examines label smoothing to improve confidence calibration in fine-tuned LLMs.
problem Improving confidence calibration in fine-tuned large language models (LLMs) after instruction tuning.
method Examine various open-sourced LLMs, label smoothing, and custom kernel design.
result Label smoothing is effective in maintaining confidence calibration but faces challenges in large vocabulary LLMs.
A reject option improves partial-label learning's accuracy.
problem Ambiguously labeled data in real-world applications.
method Risk-consistent nearest-neighbor algorithm with a reject option.
result Our method provides the best trade-off between non-rejected predictions' number and accuracy.
New priors improve Bayesian neural networks without cooling.
problem Bayesian neural networks underfit on clean datasets.
method Introduce DirClip and confidence priors to replace cooling.
result DirClip and confidence priors outperform cold posterior.
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to cons…
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, …
GUST framework improves self-training by estimating node uncertainty and generating pseudo-labels.
problem Over-confidence in pseudo-labels during self-training.
method Graph-based uncertainty-aware self-training with stochastic node labeling.
result GUST achieves state-of-the-art performance, especially in sparse labeled data settings.
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective methods, like the popular Hoeffding tree algorithm. From a statistical viewpoint, the analysis of decision tree classifiers in a streaming setti…
In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening. These methods are shown to significantly increase classification accurac…
Efficient method for multi-label text classification with large label sets.
problem Computational inefficiency in handling a large number of unique labels.
method Proposed efficient LP-ICP approach to reduce computational complexity.
result The contextualised-based classifier outperforms non-contextualised ones and achieves state-of-the-art performance.
Proposes a weighted conformal approach for cluster label uncertainty.
problem Cluster label uncertainty in unlabeled data.
method Develops a conformal inference algorithm to correct label mismatch.
result Improves confidence set size in nonlinear and high-dimensional clustering.
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.
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to…
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples to reduce cost. For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effectiv…
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels usin…
ProSelfLC improves robustness of deep neural networks by automatically deciding trust in predictions.
problem Training robust deep neural networks requires addressing issues like label noise and low entropy predictions.
method ProSelfLC progressively increases trust in predicted labels over time, considering entropy and learning time.
result ProSelfLC demonstrates improved robustness in both clean and noisy settings through empirical validation.
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.
New method corrects skewed confidence for PbN classification.
problem Weakly supervised binary classification with biased negative data.
method Corrects skewed confidence in negative data to improve classifier.
result Reduces distortion in posterior probability for PbN classification.