SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
Method evaluates classification uncertainty with adaptively chosen features.
problem Finding a balance between model efficiency and fairness.
method Adaptively selects features for equalized coverage in classification.
result Valid and effective method demonstrated on simulated and real data.
New algorithm selects genes for cancer classification using adaptive elastic net and conditional mutual information.
problem Selecting informative genes for microarray cancer classification.
method Adaptive Elastic Net with Conditional Mutual Information (AEN-CMI).
result AEN-CMI achieves the best classification performance with fewer genes.
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.
New algorithm for adaptive experimental design in scientific settings.
problem Identifying true positives while controlling false discoveries in adaptive experimental design.
method Provably sample efficient adaptive algorithm for FDR control.
result First provably sample efficient adaptive algorithm for adaptive experimental design.
A new adaptive kriging method improves binary classification of mechanical problems.
problem Efficient binary classification of mechanical problems with high fluctuation.
method Monte Carlo-intersite Voronoi (MiVor) adaptive scheme for regression surrogate model.
result The MiVor algorithm provides accurate binary classification with fewer observation points for highly fluctuating response surfaces.
A new meta-learning method for faster adaptive multiclass classification.
problem Efficiently designing multiclass classifiers that adaptively merge classes into a hierarchical structure.
method Adaptive hierarchical classification tree construction using empirical Bayes error rates and minimal spanning trees.
result The proposed method can learn faster and achieve competitive accuracy compared to existing methods.
Adapts Neyman-Pearson classification for both source and target distribution shifts.
problem Minimizing errors while controlling both Type-I and Type-II errors under distribution shifts.
method Derives an adaptive procedure that guarantees improved error rates and adapts to uninformative sources.
result Automatic adaptation to uninformative sources avoids negative transfer.
New criterion improves domain adaptation performance.
problem Binary classification in a target domain with unlabeled data and domain shift.
method Introduces a generalized Neyman-Pearson criterion for optimal domain adaptation.
result Stronger domain adaptation results possible with new criterion.
Adaptive algorithms improve cost-sensitive online classification with second-order information.
problem Improving cost-sensitive online classification with second-order information.
method Proposes adaptive regularization algorithms with sketching technique for better trade-off between performance and efficiency.
result Empirically validated algorithms' effectiveness and properties in real-world anomaly detection tasks.
New adaptive strategy for active learning with smooth boundaries.
problem Adaptive active learning in multivariate classification with unknown distributional parameters.
method Combining insights from recent works, reduction to univariate-adaptive strategies.
result Near-optimal rates achieved without prior knowledge of distributional parameters.
A new method improves EEG classification across subjects efficiently.
problem Challenges in adapting and retaining knowledge for EEG classifiers across different subjects.
method Meta UPdate Strategy (MUPS-EEG) for continuous EEG classification.
result Outperforms current state-of-the-art methods in adapting to new subjects and retaining knowledge of learned subjects.
This paper discovers classification models from sequential data without prior knowledge.
problem Lack of prior knowledge in defining kernels for online classification.
method Adapts GP-based time-series structure discovery with SMC to learn new features from sequential data.
result Improves classification accuracy by 10% on real-world data.
A novel adaptive kernel improves RBF neural networks performance.
problem Improving performance of RBF neural networks.
method Adaptive fusion of Euclidean and cosine distance measures using gradient descent.
result The method outperforms manual fusion on three major problems.
Proposes a method to directly classify target domain samples without labeled data.
problem Directly classifying unlabeled target domain samples in domain adaptation.
method Trains a classifier to classify both source and target domain samples unsupervisedly.
result Achieves state-of-the-art results in unsupervised domain adaptation tasks.
Adaptive classification methods ensure correct prediction intervals.
problem Developing methods to ensure correct prediction intervals for classification problems.
method Specialized conformal inference techniques combining cross-validation+, jackknife+, and a novel conformity score.
result The methods provide guaranteed approximate conditional coverage for complex data distributions.
We consider the problem of adaptation to the margin and to complexity in binary classification. We suggest an exponential weighting aggregation scheme. We use this aggregation procedure to construct classifiers which adapt automatically to margin and complexity. Two main examples are worked out in which adaptivity is a…
Adaptive kNN method closes the gap for unbounded support distributions.
problem Inability of standard kNN to achieve minimax optimal rate for unbounded support distributions.
method Adaptive kNN method with varying k for different samples.
result The adaptive kNN method matches the minimax lower bound.
New algorithm reduces misclassification costs in neural networks.
problem Reduces costs of misclassified instances in neural networks.
method Adaptive Cost-Sensitive Learning (AdaCSL) adjusts loss function to bridge class distribution mismatches.
result Deep neural networks with AdaCSL outperform other methods on cost-sensitive binary classification tasks.
Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.
problem Nonparametric classification under distributed differential privacy constraints.
method Minimax and adaptive transfer learning, considering privacy, sample sizes, and heterogeneity.
result Developed an adaptive classifier achieving optimal misclassification rate with privacy constraints.
Adaptive template systems improve feature extraction from persistence diagrams for machine learning.
problem Feature extraction from persistence diagrams for machine learning.
method Adaptive template systems using CDER, GMM, and HDBSCAN algorithms.
result Adaptive template systems yield competitive and often superior results in classification tasks.
Paper presents adaptive minimax risk classifiers for multidimensional concept drift.
problem Multidimensional concept drift in supervised classification.
method Adaptive minimax risk classifiers (AMRCs) tracking multivariate and high-order distribution changes.
result AMRCs provide computable tight performance guarantees and improve classification.
ALT transforms time series data for better classification.
problem Efficiently classifying time series data with varying temporal scales.
method ALT algorithm using variable-length shifted time windows.
result State-of-the-art performance with minimal computational overhead.
A new method detects unknown classes and adapts to extra dimensions in high-dimensional classification.
problem Handling unknown classes and extra variables in high-dimensional classification.
method Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA) using an EM algorithm for model estimation.
result The method can adapt to unknown classes and extra dimensions in high-dimensional data.
Adaptive algorithm for multi-objective optimization with binary constraints.
problem Optimization of black-box problems with binary constraints.
method Bayesian optimization using regression and classification models.
result Significantly faster expected hypervolume calculation.
CNAPs adapts image classifiers to new tasks efficiently.
problem Adapting image classifiers to new tasks after initial training.
method Conditional Neural Adaptive Processes (CNAPs) using a modulated classifier and adaptation network.
result CNAPs achieves state-of-the-art results on Meta-Dataset, demonstrating robust transfer-learning.
Paper detects and adapts to concept drifts in streaming data.
problem Concept drifts deteriorate classification performance over time.
method Hierarchical Hypothesis Testing (HHT) framework for detection and adaptation.
result HLFR detects and adapts to various concept drift types.
Paper studies transfer learning for nonparametric classification, establishing rates and proposing adaptive classifiers.
problem Transfer learning in nonparametric classification under different distributions.
method Established minimax rates and proposed adaptive classifiers based on weighted K-NN approach.
result Data-driven adaptive classifier achieves near-optimal rates over various parameter spaces.
IA-BMA adapts model weights to inputs for better predictions.
problem Predicting with multiple models in heterogeneous settings.
method Input adaptive Bayesian Model Averaging (IA-BMA) with an input adaptive prior and amortized variational inference.
result IA-BMA consistently delivers more accurate and better-calibrated predictions.
Adversarial domain adaptation reduces sample bias in high energy physics classifier.
problem Sample bias in high energy physics classifier training.
method Adversarial domain adaptation using neural networks with gradient reversal layer.
result Successful bias removal on simulated events at the LHC.
Enhanced binary classifier uses Urysohn's Lemma of Topology.
problem Binary classification challenges.
method Utilizes Urysohn's Lemma of Topology to construct separating functions.
result Exceptional performance in numerical experiments (95% to 100%).
Benchmark for UDA in time series classification.
problem Lack of benchmarks for unsupervised domain adaptation in time series.
method Introduces a comprehensive benchmark with new datasets and state-of-the-art neural network backbones.
result Insights into strengths and limitations of UDA methods for time series data.
We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume …
Adapts multi-armed bandits to contextual bandits using logistic regression.
problem Online decision-making with contextual information and binary rewards.
method Adapts multi-armed bandits policies to contextual bandits using logistic regression and bootstrapping.
result Adaptive-Greedy algorithm shows better performance than upper confidence bound and Thompson sampling.
Boosting ridge regression for high-dimensional data classification reduces computational cost and improves learning time.
problem High computational demand of inverting regularised covariance matrix in ridge regression for high-dimensional problems.
method Train an ensemble of ridge regressors in randomly projected subspaces, then combine them using adaptive boosting.
result Effective in terms of learning time and improved predictive performance in some cases.
Adaptive diffusions improve graph-based classification accuracy.
problem Diffusion-based classifiers' performance is affected by label propagation mechanisms specific to graphs.
method Disciplined approach to learning class-specific diffusion functions adapted to graph topology.
result Adapting diffusion functions significantly improves classification accuracy over fixed diffusions.
Adaptive transfer learning improves time series classification.
problem Improving generalization in time series classification.
method Proposed a general adaptive transfer learning framework for multi-view time series data.
result Framework maintains desirable generalization ability.
Tent adapts models during testing by minimizing entropy of predictions.
problem Adapting models to new data during testing with limited information.
method Test entropy minimization (tent) and online channel-wise affine transformations.
result Reduces generalization error on various datasets and benchmarks.
New findings link causal models to strategic classification, improving robustness and alignment.
problem Strategic adaptation by users in classification tasks.
method Causal models to bound worst-case out-of-distribution risk.
result Causal classification optimizes classification error after adaptation under certain noise conditions.
New method reduces mobile device energy for image classification.
problem High energy consumption of CNNs on mobile devices.
method Global optimization of CNN architectures with Bayesian optimization.
result Reduces energy consumption by up to 6x compared to blackbox CNNs.
Proposes GLNNs for robust semi-supervised classification using adaptive graphs.
problem Adaptive graph learning for robust semi-supervised classification.
method Optimizes graph structure from data and tasks using spectral graph theory and maximum a posteriori estimation.
result GLNNs outperform state-of-the-art approaches in semi-supervised classification.
The study examines generalization bounds for regression and classification tasks on adaptive input domains.
problem Understanding the generalization error in adaptive input domains for regression and classification.
method The analysis considers regression and classification separately, using Lipschitz continuity and 2-norm/0/1 loss for measurement. It also highlights the polynomial relationship between generalization bounds and network parameters.
result Generalization bounds for regression and classification are inversely proportional to a polynomial of the number of parameters, emphasizing the advantages of over-parameterized networks.
A new method improves few-shot image classification by updating top layers.
problem Few-shot image classification with limited data.
method Layer-wise adaptive updating (LWAU) for meta-learning.
result LWAU outperforms existing methods with a clear margin and learns more efficiently.
Unsupervised domain adaptation improves with privileged information.
problem Domain adaptation under covariate shift and overlap limitations.
method Two-stage learning algorithm inspired by expected error analysis.
result Using privileged information reduces errors and increases sample efficiency.
We study curvature-adapted submanifolds of general symmetric spaces. We generalize Cartan's theorem for isoparametric hypersurfaces of spheres and Wang's classification of isoparametric Hopf hypersurfaces in complex projective spaces to any compact symmetric space. Our second objective is to investigate such hypersurfa…
Fast and stable domain adaptation method using low-rank subspaces.
problem Improving generalization across domain boundaries in unsupervised learning.
method Calculates a single domain invariant subspace in closed form using low-rank techniques.
result Remarkable performance across various domain adaptation tasks.
Paper proposes a beta distribution method for detecting concept drift in adaptive classifiers.
problem Adaptive classifiers need to detect and respond to concept drift in real-time data streams.
method The paper introduces a beta distribution model to monitor model error and identify abnormal behavior as drift.
result The method effectively detects abrupt changes in model error, improving classifier performance.
Framework for efficient defect classification and inspection.
problem Adaptive defect classification and inspection from high volume data.
method Continual learning framework for dynamic classifier updates.
result Efficient storage and computational needs reduction.