Machine Learning

Papers
(The TQCC of Machine Learning is 5. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-02-01 to 2024-02-01.)
ArticleCitations
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods451
Learning from positive and unlabeled data: a survey216
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis139
Evaluating time series forecasting models: an empirical study on performance estimation methods96
How artificial intelligence and machine learning can help healthcare systems respond to COVID-1993
HIVE-COTE 2.0: a new meta ensemble for time series classification86
Regularisation of neural networks by enforcing Lipschitz continuity83
LoRAS: an oversampling approach for imbalanced datasets64
F*: an interpretable transformation of the F-measure57
High-dimensional Bayesian optimization using low-dimensional feature spaces49
OWL2Vec*: embedding of OWL ontologies49
Bonsai: diverse and shallow trees for extreme multi-label classification46
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics44
Loss aware post-training quantization40
How to measure uncertainty in uncertainty sampling for active learning39
The voice of optimization38
Density-based weighting for imbalanced regression37
Engineering problems in machine learning systems36
Imbalanced regression and extreme value prediction35
Interpretable clustering: an optimization approach32
Double random forest31
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams30
TRU-NET: a deep learning approach to high resolution prediction of rainfall30
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing30
Stronger data poisoning attacks break data sanitization defenses26
Global optimization based on active preference learning with radial basis functions26
Conditional variance penalties and domain shift robustness23
A deep reinforcement learning framework for continuous intraday market bidding23
Transforming variables to central normality22
Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study22
Boost image captioning with knowledge reasoning20
CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-1920
Special issue on feature engineering editorial20
Boosting Poisson regression models with telematics car driving data18
Learning programs by learning from failures18
Propositionalization and embeddings: two sides of the same coin18
Embed2Detect: temporally clustered embedded words for event detection in social media18
Tensor Q-rank: new data dependent definition of tensor rank18
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions18
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations17
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks17
Beneficial and harmful explanatory machine learning17
Scenic: a language for scenario specification and data generation16
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework16
Inductive logic programming at 3016
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning15
Large-scale pinball twin support vector machines15
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation14
Incorporating symbolic domain knowledge into graph neural networks14
Satellite derived bathymetry using deep learning14
Machine unlearning: linear filtration for logit-based classifiers13
Imputation of clinical covariates in time series13
Bandit algorithms to personalize educational chatbots13
Testing conditional independence in supervised learning algorithms13
Embedding-based Silhouette community detection12
Statistical hierarchical clustering algorithm for outlier detection in evolving data streams12
Classification with costly features as a sequential decision-making problem12
Conditional t-SNE: more informative t-SNE embeddings12
Ensembles of extremely randomized predictive clustering trees for predicting structured outputs12
Sparse classification: a scalable discrete optimization perspective11
On the sample complexity of actor-critic method for reinforcement learning with function approximation11
End-to-end entity-aware neural machine translation11
SAED: self-attentive energy disaggregation11
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models11
Toward optimal probabilistic active learning using a Bayesian approach11
Scalable Bayesian preference learning for crowds10
A study of BERT for context-aware neural machine translation10
Grounded action transformation for sim-to-real reinforcement learning10
Graph-based semi-supervised learning via improving the quality of the graph dynamically10
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping10
Traditional and context-specific spam detection in low resource settings9
Robust classification via MOM minimization9
Global optimization of objective functions represented by ReLU networks9
Beyond confusion matrix: learning from multiple annotators with awareness of instance features9
Fully convolutional open set segmentation9
A review on instance ranking problems in statistical learning9
Optimal policy trees9
Classifier calibration: a survey on how to assess and improve predicted class probabilities9
Optimal survival trees9
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification9
Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier9
SPEED: secure, PrivatE, and efficient deep learning9
A network-based positive and unlabeled learning approach for fake news detection9
Importance sampling in reinforcement learning with an estimated behavior policy9
JGPR: a computationally efficient multi-target Gaussian process regression algorithm8
Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation8
The class imbalance problem in deep learning8
VEST: automatic feature engineering for forecasting8
Deep learning and multivariate time series for cheat detection in video games8
Unsupervised representation learning with Minimax distance measures8
DIMBA: discretely masked black-box attack in single object tracking8
Linear support vector regression with linear constraints8
Lead–lag detection and network clustering for multivariate time series with an application to the US equity market8
DAFS: a domain aware few shot generative model for event detection8
Improve generated adversarial imitation learning with reward variance regularization8
Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data8
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders8
Weakly supervised change detection using guided anisotropic diffusion8
Anomaly detection with inexact labels7
Automatic discovery of interpretable planning strategies7
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment7
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning7
Relating instance hardness to classification performance in a dataset: a visual approach7
Joint optimization of an autoencoder for clustering and embedding7
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems7
Early classification of time series7
SDANet: spatial deep attention-based for point cloud classification and segmentation7
InfoGram and admissible machine learning7
Non-technical losses detection in energy consumption focusing on energy recovery and explainability7
Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification7
Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP7
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition7
RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods7
On the benefits of representation regularization in invariance based domain generalization7
Efficient fair principal component analysis7
An improved evolutionary wrapper-filter feature selection approach with a new initialisation scheme7
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification7
Can language models automate data wrangling?6
Multiscale principle of relevant information for hyperspectral image classification6
Handling epistemic and aleatory uncertainties in probabilistic circuits6
Multi-label feature ranking with ensemble methods6
Learning representations from dendrograms6
Model selection in reconciling hierarchical time series6
A generalized Weisfeiler-Lehman graph kernel6
Hierarchical optimal transport for unsupervised domain adaptation6
Distance metric learning for graph structured data6
Inductive learning of answer set programs for autonomous surgical task planning6
Safety-constrained reinforcement learning with a distributional safety critic6
Dual-domain graph convolutional networks for skeleton-based action recognition6
Stream-based active learning for sliding windows under the influence of verification latency6
Robust supervised topic models under label noise6
SLISEMAP: supervised dimensionality reduction through local explanations6
Transfer learning by mapping and revising boosted relational dependency networks6
Wavelet-packets for deepfake image analysis and detection5
Time-aware tensor decomposition for sparse tensors5
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety5
Automated adaptation strategies for stream learning5
Unified SVM algorithm based on LS-DC loss5
Considerations when learning additive explanations for black-box models5
Beyond graph neural networks with lifted relational neural networks5
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)5
Polynomial-based graph convolutional neural networks for graph classification5
TSFuse: automated feature construction for multiple time series data5
Coefficient tree regression: fast, accurate and interpretable predictive modeling5
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS)5
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation5
Incremental predictive clustering trees for online semi-supervised multi-target regression5
Online AutoML: an adaptive AutoML framework for online learning5
Binary classification with ambiguous training data5
Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach5
Skew Gaussian processes for classification5
CMD: controllable matrix decomposition with global optimization for deep neural network compression5
Protect privacy of deep classification networks by exploiting their generative power5
IntelligentPooling: practical Thompson sampling for mHealth5
A decision-theoretic approach for model interpretability in Bayesian framework5
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling5
Multi-target prediction for dummies using two-branch neural networks5
Topic extraction from extremely short texts with variational manifold regularization5
Generalizing universal adversarial perturbations for deep neural networks5
Optimal transport for conditional domain matching and label shift5
WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification5
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