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Machine Learning （ML） - April 2018, issue 4 论文列表

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卷期号： April 2018, issue 4
发布时间：
卷期年份： 2018
卷期官网： https://link.springer.com/journal/10994/volumes-and-issues/107-4
本期论文列表
Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017)

Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations

Robust Plackett–Luce model for k-ary crowdsourced preferences

Learning safe multi-label prediction for weakly labeled data

Distributed multi-task classification: a decentralized online learning approach

Crowdsourcing with unsure option

Semi-supervised AUC optimization based on positive-unlabeled learning

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning

Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation

Wasserstein discriminant analysis

Stochastic variational hierarchical mixture of sparse Gaussian processes for regression

Clustering with missing features: a penalized dissimilarity measure based approach

An adaptive heuristic for feature selection based on complementarity

LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification

Learning data discretization via convex optimization

Simple strategies for semi-supervised feature selection

When is the Naive Bayes approximation not so naive?

Emotion in reinforcement learning agents and robots: a survey

Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

Efficient benchmarking of algorithm configurators via model-based surrogates

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Instance spaces for machine learning classification

The online performance estimation framework: heterogeneous ensemble learning for data streams

Discovering predictive ensembles for transfer learning and meta-learning

Data complexity meta-features for regression problems

Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Preface to the special issue on inductive logic programming

Meta-Interpretive Learning from noisy images

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

Best-effort inductive logic programming via fine-grained cost-based hypothesis generation

Identification of biological transition systems using meta-interpreted logic programs

On better training the infinite restricted Boltzmann machines

An incremental off-policy search in a model-free Markov decision process using a single sample path

Wallenius Bayes

A scalable preference model for autonomous decision-making

Improved maximum inner product search with better theoretical guarantee using randomized partition trees

Analysis of classifiers’ robustness to adversarial perturbations

The randomized information coefficient: assessing dependencies in noisy data

Identifying and tracking topic-level influencers in the microblog streams

1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation

Manifold-based synthetic oversampling with manifold conformance estimation

Learning with rationales for document classification

Consensus-based modeling using distributed feature construction with ILP

Online multi-label dependency topic models for text classification

Simpler PAC-Bayesian bounds for hostile data

Dyad ranking using Plackett–Luce models based on joint feature representations

Introduction to the special issue on discovery science

A comparison of hierarchical multi-output recognition approaches for anuran classification

Ensembles for multi-target regression with random output selections

Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams

On analyzing user preference dynamics with temporal social networks

Discovering a taste for the unusual: exceptional models for preference mining

Targeted and contextual redescription set exploration

Probabilistic frequent subtrees for efficient graph classification and retrieval

Analyzing business process anomalies using autoencoders

Guest editors introduction to the special issue for the ECML PKDD 2018 journal track

Approximate structure learning for large Bayesian networks

Output Fisher embedding regression

Global multi-output decision trees for interaction prediction

High-dimensional penalty selection via minimum description length principle

Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes

Stagewise learning for noisy k-ary preferences

Deep Gaussian Process autoencoders for novelty detection

An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method

A new method of moments for latent variable models

A distributed Frank–Wolfe framework for learning low-rank matrices with the trace norm

Similarity encoding for learning with dirty categorical variables

ML-Plan: Automated machine learning via hierarchical planning

Inverse reinforcement learning from summary data

On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis

Learning from binary labels with instance-dependent noise

Optimizing non-decomposable measures with deep networks

Local contrast as an effective means to robust clustering against varying densities