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.
WiGS improves active learning for regression by dynamically selecting informative samples.
problem Reducing labeling costs in regression tasks.
method Formulated as a reinforcement learning problem, WiGS adapts the exploration-investigation balance.
result WiGS outperforms static methods in accuracy and labeling efficiency, especially in irregular data density.
XR improves search advertising relevance predictions.
problem Accurately predicting relevance of millions of search labels.
method eXtreme Regression (XR) with new evaluation metrics and XReg algorithm.
result XReg outperforms state-of-the-art methods by up to 50% in XR error metric.
Proposes a new method to handle noisy labels without needing accurate noise transition estimation.
problem Learning with noisy labels in the presence of class-conditional noise.
method Introduces a Latent Class-Conditional Noise (LCCN) model that embeds noise transition in a Bayesian framework and iteratively infers latent labels.
result Demonstrates superior performance compared to state-of-the-art methods on various noisy label datasets.
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 deep learning method for robust ordinal regression under label noise.
problem Label noise in real-world data constrains ordinal regression algorithms.
method Develops a deep learning approach that is robust to label noise and rank consistent.
result Demonstrates robustness to label noise and rank consistency on real data.
A new method combines experts' opinions to train regression models with noisy labels.
problem Training regression models with noisy labels from multiple experts.
method Estimate each labeler's expertise and combine opinions using learned weights.
result Empirically outperforms existing techniques on simulated and real data.
iRDM selects unlabeled samples for regression without labels, improving model accuracy.
problem Selecting unlabeled samples for regression without label information.
method Iterative representativeness-diversity maximization (iRDM).
result iRDM significantly outperforms supervised ALR, especially with limited labeled samples.
Logistic regression can handle noisy labels effectively when labels are imperfectly assigned by multiple experts.
problem Label noise in supervised classification due to manual labelling by multiple experts.
method Using approximate posterior probabilities of class membership from multiple experts to train logistic regression models.
result Logistic regression can be robust to label noise when classification difficulty is the only source of errors.
New methods improve inference with scarce labels using regression.
problem Efficient inference with limited labeled data.
method Relates PPI++ to ordinary least squares regression and uses robust regressors.
result Improved variance in estimators for few-label scenarios.
Develops a method for kernel ridge regression under covariate shift using pseudo-labels.
problem Learning a regression function with small mean squared error over a target distribution with labeled data from a different feature distribution.
method Split labeled data into two subsets, conduct kernel ridge regression on each, use imputation model to fill missing labels, and select the best candidate model.
result Non-asymptotic excess risk bounds demonstrate effective adaptation to target distribution and covariate shift.
Online active regression algorithms minimize label queries for efficient data regression.
problem Efficiently predict data points with minimal label queries in an online setting.
method Proposed online algorithms for active regression under ℓ_p loss, achieving (1+ε)-approximation with minimal label queries.
result Achieves (1+ε)-approximation with only ε^(-1) d log(nκ) label queries, matching offline methods in performance.
C-Mixup improves generalization in regression tasks by adjusting label similarity.
problem Improving generalization in regression tasks with limited data.
method C-Mixup adjusts the probability of mixing examples based on label similarity.
result C-Mixup achieves better generalization and robustness compared to vanilla mixup.
This paper investigates how transformers can learn to generalize to unseen examples in context.
problem Understanding how transformers can generalize to unseen examples in a prompt.
method Gradient descent analysis of one-layer multi-head transformers for in-context learning.
result The training loss for a one-layer multi-head transformer converges linearly to a global minimum, effectively learning ridge regression over basis functions.
CcGAN tackles conditional image generation for continuous labels.
problem Mathematical challenges in conditioning on continuous, scalar labels.
method Proposes novel empirical losses and label input methods for continuous conditional GANs.
result CcGAN generates diverse, high-quality images from continuous labels.
This paper tackles unsupervised AL for linear regression.
problem Optimally selecting samples to label from unlabeled data.
method Proposes a novel unsupervised pool-based AL approach considering informativeness, representativeness, and diversity.
result Demonstrated effectiveness on 14 datasets using three different linear regression models.
Method corrects bias in regression using simulated data and real-world gene expression data.
problem Bias in estimated effect parameters due to misclassification of class labels.
method Simulation and extrapolation method to correct bias.
result Corrected bias in estimated effect parameters.
Unified framework SVAM learns GLMs robustly to adversarial label corruption.
problem Learning GLMs under adversarial label corruption.
method SVAM framework based on variance reduction technique.
result Provable model recovery guarantees superior to state-of-the-art.
Study robustness of conformal prediction to label noise in regression and classification.
problem Robustness of conformal prediction to label noise in regression and classification.
method Characterized robustness of conformal prediction for both regression and classification problems, extending theory to control general loss functions.
result Conformal prediction and risk-controlling techniques can achieve conservative risk over clean ground truth labels with noisy labels.
Ridge regression shows different behaviors in binary classification with noisy labels.
problem Binary classification with noisy labels and anisotropic cluster distributions.
method Investigation of ridge regression behavior in overparameterized settings with label noise.
result Ridge regression exhibits qualitatively different behavior based on the scale of cluster mean vectors and covariance matrices.
We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed …
New method estimates covariance in deep heteroscedastic regression without labels.
problem Estimating covariance in deep heteroscedastic models is challenging due to sample-dependent covariance and lack of ground truth.
method Proposes a self-supervised approach using KL Divergence and 2-Wasserstein distance for covariance estimation and a neighborhood-based heuristic for pseudo labels.
result Demonstrates effective pseudo labels and a computationally cheaper yet accurate deep heteroscedastic regression.
Bayesian SSR on graphs improves regression with noisy labels.
problem Estimating function values on graphs from noisy labeled data.
method Bayesian approach using graph Laplacian and Gaussian prior.
result Rates of contraction of posterior measure around ground truth.
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.
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.
FLDCRF improves sequence labeling performance with latent dynamics interactions.
problem Sequence labeling with improved performance and latent dynamics interactions.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) with multiple latent dynamics interactions.
result FLDCRF outperforms state-of-the-art models across multiple datasets.
Improved Fréchet regression tackles noise and multicollinearity.
problem Addressing noise and multicollinearity in multi-label regression.
method Implicit regularization framework for explicit modeling of relationships.
result Effective modeling of complex dependencies without introducing biases.
Estimates GLMs robustly against label corruptions.
problem Learning GLMs under adversarial label corruptions.
method Iterative trimmed maximum likelihood estimator.
result Achieves minimax near-optimal risk.
Study investigates classification with unknown label noise in non-compact feature spaces.
problem Classification in the presence of unknown class-conditional label noise in non-compact feature spaces.
method Determines minimax optimal learning rates and presents an adaptive algorithm for classification.
result Optimal learning rates differ from those without label noise, displaying interesting threshold behavior.
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.
This work introduces a new method for coupling base and target densities in generative models.
problem Generating samples from complex target distributions using simple base distributions.
method Developed a framework of stochastic interpolants with data-dependent couplings.
result Constructing dynamical transport maps that serve as conditional generative models.
Proposes a graph-based method for active learning in regression.
problem Active learning for regression is understudied and faces challenges with noisy labels and parameter uncertainty.
method Formulates active regression as a bipartite graph optimization problem.
result Demonstrates the effectiveness of the proposed graph-based approach on benchmark data sets.
Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them…
Extends FJS analysis to general label spaces, including classification and regression.
problem Distribution shift in general label spaces, including covariate and label shifts.
method Proposes a framework for analyzing FJS in general label spaces and generalizes existing results.
result Generalizes FJS analysis to general label spaces, including classification and regression.
Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.
problem Identifying hybrid dynamical systems with nonlinear autoregressive exogenous (NARX) components and Markovian switching.
method Probabilistic framework using Expectation Maximization for parameter estimation, including submodel coefficients, hidden state values, and transition probabilities. Disentangles mode classification and NARX regression tasks. Uses soft-labels and coordinate descent approach for parameter fitting.
result Demonstrated on a SMNARX problem with three nonlinear sub-models, achieving parsimonious models through l1-norm bridge estimation and hard-thresholding.
Strategic feature manipulation helps learners identify meaningful variables in online regression settings.
problem Strategic feature manipulation poses a challenge for learners in online regression settings.
method Investigates how strategic feature manipulation by individuals can help learners recover meaningful features.
result Simple learner behavior can help accurately recover meaningful features and incentivize feature improvement.
Algorithm identifies and corrects noisy labels using Gaussian process regression.
problem Detecting and correcting real-valued noisy labels from mixed data.
method Gaussian process regression with heteroscedastic noise model and leave-one-out cross-validation.
result The method can pinpoint corrupted sample points and improve regression models.
TASFAR adapts regression models without labeled source data.
problem Lack of labeled source data for domain adaptation.
method Uses prediction confidence to estimate target label distribution and calibrate source model.
result Substantially reduces errors in various regression tasks.
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.
A novel framework for regression with multiple experts, addressing challenges in infinite and continuous label spaces.
problem Challenges in regression with multiple experts due to the infinite and continuous nature of the label space.
method Introduces a novel framework for regression with deferral, analyzing both single-stage and two-stage scenarios with new surrogate loss functions.
result Proves H-consistency bounds for both single-stage and two-stage methods, providing stronger guarantees than Bayes consistency. Proposes LRR and LRLR for improving stock prediction accuracy.
problem Improving stock prediction accuracy through nonparametric classification.
method Local radial regression and logistic regression variant.
result LRLR outperforms LPoR and MS-k-NN in real-world stock datasets. 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. 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.
Paper introduces RPWithPrior for efficient label differential privacy in regression.
problem Protecting user privacy in regression tasks with minimal accuracy loss.
method Modeling responses as continuous random variables, avoiding discretization; estimating optimal intervals for randomized responses.
result RPWithPrior algorithm guarantees ε-label differential privacy and outperforms existing methods.
Develops multi-task learning models for ordinal regression with heterogeneous data.
problem Tackling ordinal regression for heterogeneous, non-IID data.
method Sparse and deep multi-task learning approaches.
result Proposed MTOR models improve prediction performance.
New method preserves privacy by aggregating feature-vectors with weighted sums, ensuring label differential privacy.
problem Ensuring privacy in training data aggregation for sensitive labels.
method Learning from bag aggregates (LBA) with weighted Gaussian sums, preserving label differential privacy (label-DP).
result Weighted LBA using iid Gaussian weights with m randomly sampled disjoint k-sized bags provides (ε,δ)-label-DP. Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a time series. Graph-based SFA (GSFA) is a supervised extension that can solve regression problems if followed by a post-processing regression algorithm. A training graph specifies arbitrary connections between …
New model predicts multiple outputs with missing labels.
problem Missing group labels in multi-output regression.
method Weakly-supervised multi-output model using correlated Gaussian processes.
result Model excels in multi-output settings with missing labels.