Machine Learning

Article	Citations
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods	509
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis	167
Evaluating time series forecasting models: an empirical study on performance estimation methods	111
HIVE-COTE 2.0: a new meta ensemble for time series classification	109
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19	100
Regularisation of neural networks by enforcing Lipschitz continuity	94
LoRAS: an oversampling approach for imbalanced datasets	71
F*: an interpretable transformation of the F-measure	69
High-dimensional Bayesian optimization using low-dimensional feature spaces	55
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics	53
OWL2Vec*: embedding of OWL ontologies	51
How to measure uncertainty in uncertainty sampling for active learning	50
Bonsai: diverse and shallow trees for extreme multi-label classification	49
Density-based weighting for imbalanced regression	47
Imbalanced regression and extreme value prediction	45
Loss aware post-training quantization	44
The voice of optimization	41
Stronger data poisoning attacks break data sanitization defenses	37
Double random forest	36
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams	34
Interpretable clustering: an optimization approach	34
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing	34
TRU-NET: a deep learning approach to high resolution prediction of rainfall	34
Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study	29
Global optimization based on active preference learning with radial basis functions	28

Conditional variance penalties and domain shift robustness	28
Special issue on feature engineering editorial	27
A deep reinforcement learning framework for continuous intraday market bidding	25
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning	24
CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19	24
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks	24
Transforming variables to central normality	23
Propositionalization and embeddings: two sides of the same coin	22
Boosting Poisson regression models with telematics car driving data	22
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions	22
Tensor Q-rank: new data dependent definition of tensor rank	22
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations	21
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework	21
Learning programs by learning from failures	20
Boost image captioning with knowledge reasoning	20
Scenic: a language for scenario specification and data generation	20
Embed2Detect: temporally clustered embedded words for event detection in social media	19
Beneficial and harmful explanatory machine learning	19
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation	18
Inductive logic programming at 30	17
Large-scale pinball twin support vector machines	17
Machine unlearning: linear filtration for logit-based classifiers	17
Classifier calibration: a survey on how to assess and improve predicted class probabilities	16
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models	15
Satellite derived bathymetry using deep learning	15
Conditional t-SNE: more informative t-SNE embeddings	15
Incorporating symbolic domain knowledge into graph neural networks	15
Testing conditional independence in supervised learning algorithms	14
Bandit algorithms to personalize educational chatbots	14
End-to-end entity-aware neural machine translation	13
Grounded action transformation for sim-to-real reinforcement learning	13
Imputation of clinical covariates in time series	13
Statistical hierarchical clustering algorithm for outlier detection in evolving data streams	13
Sparse classification: a scalable discrete optimization perspective	12
On the sample complexity of actor-critic method for reinforcement learning with function approximation	12
Graph-based semi-supervised learning via improving the quality of the graph dynamically	12
Ensembles of extremely randomized predictive clustering trees for predicting structured outputs	12
Optimal survival trees	12
Fully convolutional open set segmentation	12
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping	12
Embedding to reference t-SNE space addresses batch effects in single-cell classification	12
Importance sampling in reinforcement learning with an estimated behavior policy	11
Embedding-based Silhouette community detection	11
A network-based positive and unlabeled learning approach for fake news detection	11
Traditional and context-specific spam detection in low resource settings	11
DAFS: a domain aware few shot generative model for event detection	11
Toward optimal probabilistic active learning using a Bayesian approach	11
SAED: self-attentive energy disaggregation	11
The class imbalance problem in deep learning	11
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification	10

Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data	10
Global optimization of objective functions represented by ReLU networks	10
Beyond confusion matrix: learning from multiple annotators with awareness of instance features	10
Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation	10
A study of BERT for context-aware neural machine translation	10
Linear support vector regression with linear constraints	9
Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification	9
Weakly supervised change detection using guided anisotropic diffusion	9
Online AutoML: an adaptive AutoML framework for online learning	9
Early classification of time series	9
Wavelet-packets for deepfake image analysis and detection	9
Deep learning and multivariate time series for cheat detection in video games	9
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification	9
SPEED: secure, PrivatE, and efficient deep learning	9
DIMBA: discretely masked black-box attack in single object tracking	9
Relating instance hardness to classification performance in a dataset: a visual approach	9
VEST: automatic feature engineering for forecasting	9
Optimal policy trees	9
A review on instance ranking problems in statistical learning	9
STUDD: a student–teacher method for unsupervised concept drift detection	9
$${{\mathrm {Latent}}Out}$$: an unsupervised deep anomaly detection approach exploiting latent space distribution	8
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition	8
Anomaly detection with inexact labels	8
Polynomial-based graph convolutional neural networks for graph classification	8
InfoGram and admissible machine learning	8
Joint optimization of an autoencoder for clustering and embedding	8
An adaptive polyak heavy-ball method	8
Improve generated adversarial imitation learning with reward variance regularization	8
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders	8
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety	8
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning	8
JGPR: a computationally efficient multi-target Gaussian process regression algorithm	8
Can language models automate data wrangling?	8
Unsupervised representation learning with Minimax distance measures	8
Analyzing and repairing concept drift adaptation in data stream classification	8
Efficient fair principal component analysis	8
Lead–lag detection and network clustering for multivariate time series with an application to the US equity market	8
Scrutinizing XAI using linear ground-truth data with suppressor variables	7
Dual-domain graph convolutional networks for skeleton-based action recognition	7
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems	7
Handling epistemic and aleatory uncertainties in probabilistic circuits	7
IntelligentPooling: practical Thompson sampling for mHealth	7
Non-technical losses detection in energy consumption focusing on energy recovery and explainability	7
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment	7
An improved evolutionary wrapper-filter feature selection approach with a new initialisation scheme	7
Ada-boundary: accelerating DNN training via adaptive boundary batch selection	7
Beyond graph neural networks with lifted relational neural networks	7
RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods	7
SDANet: spatial deep attention-based for point cloud classification and segmentation	7
SLISEMAP: supervised dimensionality reduction through local explanations	7
On the benefits of representation regularization in invariance based domain generalization	7
Unified SVM algorithm based on LS-DC loss	7
Safety-constrained reinforcement learning with a distributional safety critic	7
Stream-based active learning for sliding windows under the influence of verification latency	7
Automatic discovery of interpretable planning strategies	7
Deep multimodal representation learning for generalizable person re-identification	7
Transfer learning by mapping and revising boosted relational dependency networks	7
Robust supervised topic models under label noise	6
WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification	6
Distance metric learning for graph structured data	6
Metrics and methods for robustness evaluation of neural networks with generative models	6
Model selection in reconciling hierarchical time series	6
Efficient learning of large sets of locally optimal classification rules	6
Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks	6
Domain adversarial neural networks for domain generalization: when it works and how to improve	6
Protect privacy of deep classification networks by exploiting their generative power	6
Learning representations from dendrograms	6
Automated adaptation strategies for stream learning	6
Ordinal regression with explainable distance metric learning based on ordered sequences	6
A generalized Weisfeiler-Lehman graph kernel	6
Hierarchical optimal transport for unsupervised domain adaptation	6
Multi-label feature ranking with ensemble methods	6
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS)	6
Inductive learning of answer set programs for autonomous surgical task planning	6
Understanding CNN fragility when learning with imbalanced data	6
MLife: a lite framework for machine learning lifecycle initialization	6
Multiscale principle of relevant information for hyperspectral image classification	6
Binary classification with ambiguous training data	5
A survey of class-imbalanced semi-supervised learning	5
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces	5

Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling	5
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)	5
Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach	5
Optimal transport for conditional domain matching and label shift	5
CMD: controllable matrix decomposition with global optimization for deep neural network compression	5
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation	5
Pruning convolutional neural networks via filter similarity analysis	5
Generating contrastive explanations for inductive logic programming based on a near miss approach	5
Stateless neural meta-learning using second-order gradients	5
Considerations when learning additive explanations for black-box models	5
Clustered and deep echo state networks for signal noise reduction	5
Multi-target prediction for dummies using two-branch neural networks	5
An instance-dependent simulation framework for learning with label noise	5
Re-thinking model robustness from stability: a new insight to defend adversarial examples	5
Federated learning with superquantile aggregation for heterogeneous data	5
Embedding and extraction of knowledge in tree ensemble classifiers	5
Coefficient tree regression: fast, accurate and interpretable predictive modeling	5
Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification	5
The flowing nature matters: feature learning from the control flow graph of source code for bug localization	5
Topic extraction from extremely short texts with variational manifold regularization	5
Incremental predictive clustering trees for online semi-supervised multi-target regression	5
Bimodal variational autoencoder for audiovisual speech recognition	5
Lipschitzness is all you need to tame off-policy generative adversarial imitation learning	5
Generalizing universal adversarial perturbations for deep neural networks	5
Skew Gaussian processes for classification	5
Time-aware tensor decomposition for sparse tensors	5
A decision-theoretic approach for model interpretability in Bayesian framework	5
TSFuse: automated feature construction for multiple time series data	5
Explainable online ensemble of deep neural network pruning for time series forecasting	5
Multiway p-spectral graph cuts on Grassmann manifolds	5
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model	5