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Machine Learning （ML） - April 2022, 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 2022, issue 4
发布时间：
卷期年份： 2022
卷期官网： https://link.springer.com/journal/10994/volumes-and-issues/111-4
本期论文列表
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