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Machine Learning （ML） - April 2021, issue 4 论文列表

点击这里查看 Machine Learning 的JCR分区、影响因子等信息
卷期号： April 2021, issue 4
发布时间：
卷期年份： 2021
卷期官网： https://link.springer.com/journal/10994/volumes-and-issues/110-4
本期论文列表
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