An extended DEIM algorithm for subset selection and class identification
Protect privacy of deep classification networks by exploiting their generative power
SPEED: secure, PrivatE, and efficient deep learning
Beneficial and harmful explanatory machine learning
Probabilistic inductive constraint logic
Top program construction and reduction for polynomial time Meta-Interpretive learning
AUTOMAT[R]IX: learning simple matrix pipelines
Learning programs by learning from failures
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations
Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification
TRU-NET: a deep learning approach to high resolution prediction of rainfall
On testing transitivity in online preference learning
Variational learning from implicit bandit feedback
Testing conditional independence in supervised learning algorithms
AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow
Sampled Gromov Wasserstein
Density-based weighting for imbalanced regression
Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting
Information-theoretic regularization for learning global features by sequential VAE
Convex optimization with an interpolation-based projection and its application to deep learning
MLife: a lite framework for machine learning lifecycle initialization
Tensor decision trees for continual learning from drifting data streams
Deep learning and multivariate time series for cheat detection in video games
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Data driven conditional optimal transport
Misalignment problem in matrix decomposition with missing values
Sparse classification: a scalable discrete optimization perspective
HIVE-COTE 2.0: a new meta ensemble for time series classification
Loss aware post-training quantization
The voice of optimization
LoRAS: an oversampling approach for imbalanced datasets
Conditional variance penalties and domain shift robustness
Kernel machines for current status data
Regularisation of neural networks by enforcing Lipschitz continuity
Global optimization based on active preference learning with radial basis functions
Correction to: Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19
CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19
Node classification over bipartite graphs through projection
Interpretable clustering: an optimization approach
Statistical hierarchical clustering algorithm for outlier detection in evolving data streams
Imputation of clinical covariates in time series
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels
Incorporating symbolic domain knowledge into graph neural networks
Learning hierarchical probabilistic logic programs
Beyond graph neural networks with lifted relational neural networks
Inductive learning of answer set programs for autonomous surgical task planning
Distance metric learning for graph structured data
OWL2Vec*: embedding of OWL ontologies
A comparison of statistical relational learning and graph neural networks for aggregate graph queries
Tensor Q-rank: new data dependent definition of tensor rank
Joint optimization of an autoencoder for clustering and embedding
Linear support vector regression with linear constraints
Pseudo-marginal Bayesian inference for Gaussian process latent variable models
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training
Toward optimal probabilistic active learning using a Bayesian approach
Multi-objective multi-armed bandit with lexicographically ordered and satisficing objectives
Importance sampling in reinforcement learning with an estimated behavior policy
Efficient Weingarten map and curvature estimation on manifolds
Graph-based semi-supervised learning via improving the quality of the graph dynamically
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions
Automated adaptation strategies for stream learning
Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders
Early classification of time series
Multiple clusterings of heterogeneous information networks
MODES: model-based optimization on distributed embedded systems
F*: an interpretable transformation of the F-measure
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Reshaped tensor nuclear norms for higher order tensor completion
Coupling matrix manifolds assisted optimization for optimal transport problems
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling
Correction to: Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach
Bayesian optimization with approximate set kernels
QuicK-means: accelerating inference for K-means by learning fast transforms
Robust supervised topic models under label noise
Convex programming based spectral clustering
Large scale multi-label learning using Gaussian processes
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification
Topic extraction from extremely short texts with variational manifold regularization
Adaptive covariate acquisition for minimizing total cost of classification
Ordinal regression with explainable distance metric learning based on ordered sequences
Provable training set debugging for linear regression
RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods
ZipLine: an optimized algorithm for the elastic bulk synchronous parallel model
Conditional t-SNE: more informative t-SNE embeddings
Robust non-parametric regression via incoherent subspace projections
Introduction to the special issue of the ECML PKDD 2021 journal track
Guest editorial: special issue on reinforcement learning for real life
Inverse reinforcement learning in contextual MDPs
A deep reinforcement learning framework for continuous intraday market bidding
Bandit algorithms to personalize educational chatbots
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Grounded action transformation for sim-to-real reinforcement learning
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems
Lessons on off-policy methods from a notification component of a chatbot
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation
Automatic discovery of interpretable planning strategies
IntelligentPooling: practical Thompson sampling for mHealth
