Polynomial-based graph convolutional neural networks for graph classification
Few-shot learning for spatial regression via neural embedding-based Gaussian processes
Handling epistemic and aleatory uncertainties in probabilistic circuits
Lifting symmetry breaking constraints with inductive logic programming
SDANet: spatial deep attention-based for point cloud classification and segmentation
Efficient SVDD sampling with approximation guarantees for the decision boundary
Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks
Time-aware tensor decomposition for sparse tensors
Lipschitzness is all you need to tame off-policy generative adversarial imitation learning
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning
Improving sequential latent variable models with autoregressive flows
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)
A taxonomy of weight learning methods for statistical relational learning
Maintaining AUC and H-measure over time
Optimised one-class classification performance
Clustered and deep echo state networks for signal noise reduction
High-dimensional correlation matrix estimation for general continuous data with Bagging technique
Context-aware spatio-temporal event prediction via convolutional Hawkes processes
Optimal survival trees
Adaptive infinite dropout for noisy and sparse data streams
Correction to: Meta-interpretive learning as metarule specialisation
World-class interpretable poker
Relating instance hardness to classification performance in a dataset: a visual approach
A review on instance ranking problems in statistical learning
Reinforcement learning for robotic manipulation using simulated locomotion demonstrations
Non-technical losses detection in energy consumption focusing on energy recovery and explainability
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks
Generalized vec trick for fast learning of pairwise kernel models
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment
Symbolic DNN-Tuner
Multi-target prediction for dummies using two-branch neural networks
Achieving adversarial robustness via sparsity
A flexible class of dependence-aware multi-label loss functions
Model selection in reconciling hierarchical time series
Multiway p-spectral graph cuts on Grassmann manifolds
Stronger data poisoning attacks break data sanitization defenses
Embed2Detect: temporally clustered embedded words for event detection in social media
How to measure uncertainty in uncertainty sampling for active learning
Learning from interpretation transition using differentiable logic programming semantics
Inductive logic programming at 30
SAMBA: safe model-based & active reinforcement learning
InfoGram and admissible machine learning
Boosting Poisson regression models with telematics car driving data
ReliefE: feature ranking in high-dimensional spaces via manifold embeddings
Smoothing graphons for modelling exchangeable relational data
Understanding generalization error of SGD in nonconvex optimization
Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders
Dual-domain graph convolutional networks for skeleton-based action recognition
Robustness verification of ReLU networks via quadratic programming
Learning explanations for biological feedback with delays using an event calculus
Re-thinking model robustness from stability: a new insight to defend adversarial examples
Traditional and context-specific spam detection in low resource settings
Arbitrary conditional inference in variational autoencoders via fast prior network training
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
A generalized Weisfeiler-Lehman graph kernel
Variance reduction in feature hashing using MLE and control variate method
Large scale tensor regression using kernels and variational inference
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing
Optimal policy trees
JGPR: a computationally efficient multi-target Gaussian process regression algorithm
Stream-based active learning for sliding windows under the influence of verification latency
Randomized approximate class-specific kernel spectral regression analysis for large-scale face verification
Policy space identification in configurable environments
Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation
The backbone method for ultra-high dimensional sparse machine learning
Nyström landmark sampling and regularized Christoffel functions
Planning for potential: efficient safe reinforcement learning
DEFT: distilling entangled factors by preventing information diffusion
Fast spectral analysis for approximate nearest neighbor search
Online active classification via margin-based and feature-based label queries
Modelling spatiotemporal dynamics from Earth observation data with neural differential equations
CMD: controllable matrix decomposition with global optimization for deep neural network compression
The flowing nature matters: feature learning from the control flow graph of source code for bug localization
Online strongly convex optimization with unknown delays
On the benefits of representation regularization in invariance based domain generalization
A study of BERT for context-aware neural machine translation
Worst-case regret analysis of computationally budgeted online kernel selection
Improve generated adversarial imitation learning with reward variance regularization
Improving kernel online learning with a snapshot memory
Bayesian optimization with partially specified queries
Multiple partitions alignment via spectral rotation
Switching: understanding the class-reversed sampling in tail sample memorization
Improving deep label noise learning with dual active label correction
Exploring the common principal subspace of deep features in neural networks
Towards interpreting deep neural networks via layer behavior understanding
End-to-end entity-aware neural machine translation
Robust linear classification from limited training data
Optimal transport for conditional domain matching and label shift
Nested aggregation of experts using inducing points for approximated Gaussian process regression
Partitioned hybrid learning of Bayesian network structures
Semi-supervised Latent Block Model with pairwise constraints
Semi-Lipschitz functions and machine learning for discrete dynamical systems on graphs
Generating contrastive explanations for inductive logic programming based on a near miss approach
MAGMA: inference and prediction using multi-task Gaussian processes with common mean
Order preserving hierarchical agglomerative clustering
Scrutinizing XAI using linear ground-truth data with suppressor variables
Embedding and extraction of knowledge in tree ensemble classifiers
One-Stage Tree: end-to-end tree builder and pruner
Analyzing and repairing concept drift adaptation in data stream classification
Large-scale pinball twin support vector machines
A network-based positive and unlabeled learning approach for fake news detection
Learning any memory-less discrete semantics for dynamical systems represented by logic programs
Efficient fair principal component analysis
Meta-interpretive learning as metarule specialisation
Semi-parametric Bayes regression with network-valued covariates
Robust reputation independence in ranking systems for multiple sensitive attributes
Lifted model checking for relational MDPs
A unified framework for online trip destination prediction
Greedy structure learning from data that contain systematic missing values
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS)
Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study
Pruning convolutional neural networks via filter similarity analysis
Towards harnessing feature embedding for robust learning with noisy labels
Machine unlearning: linear filtration for logit-based classifiers
Stateless neural meta-learning using second-order gradients
An adaptive polyak heavy-ball method
The pure exploration problem with general reward functions depending on full distributions
Wasserstein-based fairness interpretability framework for machine learning models
Stabilize deep ResNet with a sharp scaling factor \(\tau\)
Recursive tree grammar autoencoders
On the robustness of randomized classifiers to adversarial examples
Explainable online ensemble of deep neural network pruning for time series forecasting
