MLJ 2018

71 papers

1-Bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation Vincent Cottet, Pierre Alquier

A Comparison of Hierarchical Multi-Output Recognition Approaches for Anuran Classification Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura

A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm Wenjie Zheng, Aurélien Bellet, Patrick Gallinari

A New Method of Moments for Latent Variable Models Matteo Ruffini, Marta Casanellas, Ricard Gavaldà

A Scalable Preference Model for Autonomous Decision-Making Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes

Accurate Parameter Estimation for Bayesian Network Classifiers Using Hierarchical Dirichlet Processes François Petitjean, Wray L. Buntine, Geoffrey I. Webb, Nayyar Abbas Zaidi

An Adaptive Heuristic for Feature Selection Based on Complementarity Sumanta Singha, Prakash P. Shenoy

An Incremental Off-Policy Search in a Model-Free Markov Decision Process Using a Single Sample Path Ajin George Joseph, Shalabh Bhatnagar

An Online Prediction Algorithm for Reinforcement Learning with Linear Function Approximation Using Cross Entropy Method Ajin George Joseph, Shalabh Bhatnagar

Analysis of Classifiers' Robustness to Adversarial Perturbations Alhussein Fawzi, Omar Fawzi, Pascal Frossard

Analyzing Business Process Anomalies Using Autoencoders Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser

Approximate Structure Learning for Large Bayesian Networks Mauro Scanagatta, Giorgio Corani, Cassio Polpo de Campos, Marco Zaffalon

Best-Effort Inductive Logic Programming via Fine-Grained Cost-Based Hypothesis Generation - The Inspire System at the Inductive Logic Programming Competition Peter Schüller, Mishal Benz

Bootstrapping the Out-of-Sample Predictions for Efficient and Accurate Cross-Validation Ioannis Tsamardinos, Elissavet Greasidou, Giorgos Borboudakis

Clustering with Missing Features: A Penalized Dissimilarity Measure Based Approach Shounak Datta, Supritam Bhattacharjee, Swagatam Das

Consensus-Based Modeling Using Distributed Feature Construction with ILP Haimonti Dutta, Ashwin Srinivasan

Correction to: Semi-Supervised AUC Optimization Based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu, Masashi Sugiyama

Crowdsourcing with Unsure Option Yao-Xiang Ding, Zhi-Hua Zhou

Data Complexity Meta-Features for Regression Problems Ana Carolina Lorena, Aron I. Maciel, Péricles Barbosa C. de Miranda, Ivan G. Costa, Ricardo B. C. Prudêncio

Deep Gaussian Process Autoencoders for Novelty Detection Remi Domingues, Pietro Michiardi, Jihane Zouaoui, Maurizio Filippone

Discovering a Taste for the Unusual: Exceptional Models for Preference Mining Cláudio Rebelo de Sá, Wouter Duivesteijn, Paulo J. Azevedo, Alípio Mário Jorge, Carlos Soares, Arno J. Knobbe

Discovering Predictive Ensembles for Transfer Learning and Meta-Learning Pavel Kordík, Ján Cerný, Tomás Frýda

Distributed Multi-Task Classification: A Decentralized Online Learning Approach Chi Zhang, Peilin Zhao, Shuji Hao, Yeng Chai Soh, Bu-Sung Lee, Chunyan Miao, Steven C. H. Hoi

Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations Dirk Schäfer, Eyke Hüllermeier

Efficient Benchmarking of Algorithm Configurators via Model-Based Surrogates Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

Efficient Preconditioning for Noisy Separable Nonnegative Matrix Factorization Problems by Successive Projection Based Low-Rank Approximations Tomohiko Mizutani, Mirai Tanaka

Emotion in Reinforcement Learning Agents and Robots: A Survey Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker

Empirical Hardness of Finding Optimal Bayesian Network Structures: Algorithm Selection and Runtime Prediction Brandon M. Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki

Ensembles for Multi-Target Regression with Random Output Selections Martin Breskvar, Dragi Kocev, Saso Dzeroski

Global Multi-Output Decision Trees for Interaction Prediction Konstantinos Pliakos, Pierre Geurts, Celine Vens

High-Dimensional Penalty Selection via Minimum Description Length Principle Kohei Miyaguchi, Kenji Yamanishi

Identification of Biological Transition Systems Using Meta-Interpreted Logic Programs Michael Bain, Ashwin Srinivasan

Identifying and Tracking Topic-Level Influencers in the Microblog Streams Sen Su, Yakun Wang, Zhongbao Zhang, Cheng Chang, Muhammad Azam Zia

Improved Maximum Inner Product Search with Better Theoretical Guarantee Using Randomized Partition Trees Omid Keivani, Kaushik Sinha, Parikshit Ram

Instance Spaces for Machine Learning Classification Mario A. Muñoz, Laura Villanova, Davaatseren Baatar, Kate Smith-Miles

Inverse Reinforcement Learning from Summary Data Antti Kangasrääsiö, Samuel Kaski

Learning Data Discretization via Convex Optimization Vojtech Franc, Ondrej Fikar, Karel Bartos, Michal Sofka

Learning from Binary Labels with Instance-Dependent Noise Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan

Learning Safe Multi-Label Prediction for Weakly Labeled Data Tong Wei, Lan-Zhe Guo, Yufeng Li, Wei Gao

Learning with Rationales for Document Classification Manali Sharma, Mustafa Bilgic

Local Contrast as an Effective Means to Robust Clustering Against Varying Densities Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu

LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification Subhadeep Mukhopadhyay, Shinjini Nandi

Manifold-Based Synthetic Oversampling with Manifold Conformance Estimation Colin Bellinger, Christopher Drummond, Nathalie Japkowicz

Meta-Interpretive Learning from Noisy Images Stephen H. Muggleton, Wang-Zhou Dai, Claude Sammut, Alireza Tamaddoni-Nezhad, Jing Wen, Zhi-Hua Zhou

Meta-QSAR: A Large-Scale Application of Meta-Learning to Drug Design and Discovery Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King

Metalearning and Algorithm Selection: Progress, State of the Art and Introduction to the 2018 Special Issue Pavel Brazdil, Christophe G. Giraud-Carrier

ML-Plan: Automated Machine Learning via Hierarchical Planning Felix Mohr, Marcel Wever, Eyke Hüllermeier

On Analyzing User Preference Dynamics with Temporal Social Networks Fabíola Souza F. Pereira, João Gama, Sandra de Amo, Gina M. B. Oliveira

On Better Training the Infinite Restricted Boltzmann Machines Xuan Peng, Xunzhang Gao, Xiang Li

On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis Vitalik Melnikov, Eyke Hüllermeier

Online Multi-Label Dependency Topic Models for Text Classification Sophie Burkhardt, Stefan Kramer

Optimizing Non-Decomposable Measures with Deep Networks Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani

Output Fisher Embedding Regression Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc

Probabilistic Frequent Subtrees for Efficient Graph Classification and Retrieval Pascal Welke, Tamás Horváth, Stefan Wrobel

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams Ali Pesaranghader, Herna L. Viktor, Eric Paquet

Robust Plackett-Luce Model for K-Ary Crowdsourced Preferences Bo Han, Yuangang Pan, Ivor W. Tsang

Scalable Gaussian Process-Based Transfer Surrogates for Hyperparameter Optimization Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

Semi-Supervised AUC Optimization Based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu, Masashi Sugiyama

Similarity Encoding for Learning with Dirty Categorical Variables Patricio Cerda, Gaël Varoquaux, Balázs Kégl

Simple Strategies for Semi-Supervised Feature Selection Konstantinos Sechidis, Gavin Brown

Simpler PAC-Bayesian Bounds for Hostile Data Pierre Alquier, Benjamin Guedj

Speeding up Algorithm Selection Using Average Ranking and Active Testing by Introducing Runtime Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren

Stagewise Learning for Noisy K-Ary Preferences Yuangang Pan, Bo Han, Ivor W. Tsang

Stochastic Variational Hierarchical Mixture of Sparse Gaussian Processes for Regression Thi Nhat Anh Nguyen, Abdesselam Bouzerdoum, Son Lam Phung

Targeted and Contextual Redescription Set Exploration Matej Mihelcic, Tomislav Smuc

The Online Performance Estimation Framework: Heterogeneous Ensemble Learning for Data Streams Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren

The Randomized Information Coefficient: Assessing Dependencies in Noisy Data Simone Romano, Xuan Vinh Nguyen, Karin Verspoor, James Bailey

Ultra-Strong Machine Learning: Comprehensibility of Programs Learned with ILP Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold

Wallenius Bayes Enric Junqué de Fortuny, David Martens, Foster J. Provost

Wasserstein Discriminant Analysis Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy

When Is the Naive Bayes Approximation Not so Naive? Christopher R. Stephens, Hugo Flores Huerta, Ana Ruiz Linares