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ORCIDMachine Learning, Volume 109
Volume 109, Number 1, January 2020
- Vojtech Kovarík
, Viliam Lisý:
Analysis of Hannan consistent selection for Monte Carlo tree search in simultaneous move games. 1-50 - Harold S. Connamacher, Nikil Pancha, Rui Liu, Soumya Ray:
Rankboost+: an improvement to Rankboost. 51-78 - Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt:
Combining Bayesian optimization and Lipschitz optimization. 79-102 - Huan Li, Zhouchen Lin:
Provable accelerated gradient method for nonconvex low rank optimization. 103-134 - Mattia Desana, Christoph Schnörr:
Sum-product graphical models. 135-173 - Alberto Cano, Bartosz Krawczyk:
Kappa Updated Ensemble for drifting data stream mining. 175-218
Volume 109, Number 2, February 2020
- Jia He, Changying Du, Fuzhen Zhuang, Xin Yin, Qing He, Guoping Long:
Online Bayesian max-margin subspace learning for multi-view classification and regression. 219-249 - Nastasiya F. Grinberg, Oghenejokpeme I. Orhobor, Ross D. King:
An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat. 251-277 - Soham Sarkar, Rahul Biswas, Anil Kumar Ghosh:
On some graph-based two-sample tests for high dimension, low sample size data. 279-306 - Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt:
Predictive spreadsheet autocompletion with constraints. 307-325 - Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki:
A bad arm existence checking problem: How to utilize asymmetric problem structure? 327-372 - Jesper E. van Engelen, Holger H. Hoos:
A survey on semi-supervised learning. 373-440
Volume 109, Number 3, March 2020
- Kee-Eung Kim, Jun Zhu:
Foreword: special issue for the journal track of the 11th Asian Conference on Machine Learning (ACML 2019). 441-443 - Wenzhang Zhuge, Chenping Hou, Shaoliang Peng, Dongyun Yi:
Joint consensus and diversity for multi-view semi-supervised classification. 445-465 - Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Gradient descent optimizes over-parameterized deep ReLU networks. 467-492 - Nicolas Bougie, Ryutaro Ichise:
Skill-based curiosity for intrinsically motivated reinforcement learning. 493-512 - Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik:
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. 513-532 - Peng Zhao, Le-Wen Cai, Zhi-Hua Zhou:
Handling concept drift via model reuse. 533-568 - Qiang Zhou, Yu Chen, Sinno Jialin Pan:
Communication-efficient distributed multi-task learning with matrix sparsity regularization. 569-601 - Longhao Yuan, Chao Li, Jianting Cao, Qibin Zhao:
Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion. 603-622 - Zhi-Hao Tan, Peng Tan, Yuan Jiang, Zhi-Hua Zhou:
Multi-label optimal margin distribution machine. 623-642 - Han-Jia Ye, Xiang-Rong Sheng, De-Chuan Zhan:
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. 643-664
Volume 109, Number 4, April 2020
- Esteban G. Tabak, Giulio Trigila, Wenjun Zhao:
Conditional density estimation and simulation through optimal transport. 665-688 - Edwin Simpson, Iryna Gurevych:
Scalable Bayesian preference learning for crowds. 689-718 - Jessa Bekker, Jesse Davis:
Learning from positive and unlabeled data: a survey. 719-760 - Artür Manukyan, Elvan Ceyhan:
Classification using proximity catch digraphs. 761-811 - Ching-Pei Lee, Kai-Wei Chang:
Distributed block-diagonal approximation methods for regularized empirical risk minimization. 813-852 - Johannes Fürnkranz, Tomás Kliegr, Heiko Paulheim:
On cognitive preferences and the plausibility of rule-based models. 853-898
Volume 109, Number 5, May 2020
- Tianbao Yang, Lijun Zhang, Qihang Lin, Shenghuo Zhu, Rong Jin:
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity. 899-938 - Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu:
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. 939-972 - Dimitris Bertsimas, Bart P. G. Van Parys:
Sparse hierarchical regression with polynomials. 973-997 - Alberto N. Escalante-B., Laurenz Wiskott:
Improved graph-based SFA: information preservation complements the slowness principle. 999-1037 - Dan Halbersberg, Maydan Wienreb, Boaz Lerner:
Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier. 1039-1099 - Mariam Kiran, Samir Khan:
Editorial: Machine learning for safety-critical applications in engineering. 1101-1102 - Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae:
Engineering problems in machine learning systems. 1103-1126 - Mariam Kiran, Cong Wang, George Papadimitriou, Anirban Mandal, Ewa Deelman:
Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP. 1127-1143
Volume 109, Number 6, June 2020
- Takuya Kida, Tetsuji Kuboyama, Takeaki Uno, Akihiro Yamamoto:
Guest Editorial: Special issue on Discovery Science. 1145-1146 - Daniel Trabold, Tamás Horváth, Stefan Wrobel:
Effective approximation of parametrized closure systems over transactional data streams. 1147-1177 - Matej Petkovic, Dragi Kocev, Saso Dzeroski:
Feature ranking for multi-target regression. 1179-1204 - Livio Bioglio, Valentina Rho, Ruggero G. Pensa:
Ranking by inspiration: a network science approach. 1205-1229 - Gianvito Pio, Michelangelo Ceci, Francesca Prisciandaro, Donato Malerba:
Exploiting causality in gene network reconstruction based on graph embedding. 1231-1279
Volume 109, Number 7, July 2020
- Peter A. Flach:
Reflections on reciprocity in research. 1281-1285 - Dimitar Kazakov, Filip Zelezný:
Guest editors' introduction: special issue on Inductive Logic Programming (ILP 2019). 1287-1288 - Andrew Cropper, Rolf Morel, Stephen H. Muggleton:
Learning higher-order logic programs. 1289-1322 - Andrew Cropper, Sophie Tourret:
Logical reduction of metarules. 1323-1369 - Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff:
Constructing generative logical models for optimisation problems using domain knowledge. 1371-1392 - Andrew Cropper, Richard Evans, Mark Law:
Inductive general game playing. 1393-1434 - Rodrigo Azevedo Santos, Aline Paes, Gerson Zaverucha:
Transfer learning by mapping and revising boosted relational dependency networks. 1435-1463 - Nada Lavrac, Blaz Skrlj, Marko Robnik-Sikonja:
Propositionalization and embeddings: two sides of the same coin. 1465-1507
Volume 109, Number 8, August 2020
- Adriano Rivolli, Jesse Read, Carlos Soares, Bernhard Pfahringer, André C. P. L. F. de Carvalho:
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. 1509-1563 - Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown:
Correction to: Efficient feature selection using shrinkage estimators. 1565-1567 - Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee:
Double random forest. 1569-1586 - Jaromír Janisch, Tomás Pevný, Viliam Lisý:
Classification with costly features as a sequential decision-making problem. 1587-1615 - Tomoharu Iwata, Machiko Toyoda, Shotaro Tora, Naonori Ueda:
Anomaly detection with inexact labels. 1617-1633 - Guillaume Lecué, Matthieu Lerasle, Timothée Mathieu:
Robust classification via MOM minimization. 1635-1665 - Guillaume Lecué, Matthieu Lerasle, Timothée Mathieu:
Correction to: Robust classification via MOM minimization. 1667 - Sarang Kapoor, Dhish Kumar Saxena, Matthijs van Leeuwen:
Discovering subjectively interesting multigraph patterns. 1669-1696
Volume 109, Number 9-10, September 2020
- Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier:
Introduction to the special issue of the ECML PKDD 2020 journal track. 1697-1698 - Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active deep Q-learning with demonstration. 1699-1725 - Emanuele Pesce, Giovanni Montana:
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication. 1727-1747 - Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum:
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition. 1749-1778 - Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani:
Learning representations from dendrograms. 1779-1802 - Rita P. Ribeiro, Nuno Moniz:
Imbalanced regression and extreme value prediction. 1803-1835 - Hwanjun Song, Sundong Kim, Minseok Kim, Jae-Gil Lee:
Ada-boundary: accelerating DNN training via adaptive boundary batch selection. 1837-1853 - Homayun Afrabandpey, Tomi Peltola, Juho Piironen, Aki Vehtari, Samuel Kaski:
A decision-theoretic approach for model interpretability in Bayesian framework. 1855-1876 - Alessio Benavoli, Dario Azzimonti, Dario Piga:
Skew Gaussian processes for classification. 1877-1902 - Soma Yokoi, Takuma Otsuka, Issei Sato:
Weak approximation of transformed stochastic gradient MCMC. 1903-1923 - Riccardo Moriconi, Marc Peter Deisenroth, K. S. Sesh Kumar:
High-dimensional Bayesian optimization using low-dimensional feature spaces. 1925-1943 - Baptiste Bauvin, Cécile Capponi, Jean-Francis Roy, François Laviolette:
Fast greedy C-bound minimization with guarantees. 1945-1986
Volume 109, Number 11, November 2020
- Foster J. Provost:
In memory of Tom Fawcett. 1987-1992 - Larisa N. Soldatova, Joaquin Vanschoren:
Guest editors' introduction to the special issue on Discovery Science. 1993-1995 - Vítor Cerqueira, Luís Torgo, Igor Mozetic:
Evaluating time series forecasting models: an empirical study on performance estimation methods. 1997-2028 - Zahraa S. Abdallah, Mohamed Medhat Gaber:
Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series. 2029-2061 - Morteza Haghir Chehreghani:
Unsupervised representation learning with Minimax distance measures. 2063-2097 - Sujay Khandagale, Han Xiao, Rohit Babbar:
Bonsai: diverse and shallow trees for extreme multi-label classification. 2099-2119 - Aljaz Osojnik, Pance Panov, Saso Dzeroski:
Incremental predictive clustering trees for online semi-supervised multi-target regression. 2121-2139 - Matej Petkovic, Saso Dzeroski, Dragi Kocev:
Multi-label feature ranking with ensemble methods. 2141-2159 - Blaz Skrlj, Jan Kralj, Nada Lavrac:
Embedding-based Silhouette community detection. 2161-2193 - Oghenejokpeme I. Orhobor, Nickolai N. Alexandrov, Ross D. King:
Predicting rice phenotypes with meta and multi-target learning. 2195-2212 - Dragi Kocev, Michelangelo Ceci, Tomaz Stepisnik:
Ensembles of extremely randomized predictive clustering trees for predicting structured outputs. 2213-2241
Volume 109, Number 12, December 2020
- Kee-Eung Kim, Vineeth N. Balasubramanian:
Foreword: special issue for the journal track of the 12th Asian conference on machine learning (ACML 2020). 2243-2245 - Jieting Wang, Yuhua Qian, Feijiang Li:
Learning with mitigating random consistency from the accuracy measure. 2247-2281 - Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu:
Robust high dimensional expectation maximization algorithm via trimmed hard thresholding. 2283-2311 - Feicheng Huang, Zhixin Li, Haiyang Wei, Canlong Zhang, Huifang Ma:
Boost image captioning with knowledge reasoning. 2313-2332 - Jinhong Jung, Lee Sael:
Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach. 2333-2347 - Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang:
Spanning attack: reinforce black-box attacks with unlabeled data. 2349-2368 - Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama:
Binary classification with ambiguous training data. 2369-2388
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