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ORCIDMachine Learning, Volume 110
Volume 110, Number 1, January 2021
- Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood
, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole:
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. 1-14 - Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19. 15-35 - Marija Stankova, Stiene Praet, David Martens, Foster J. Provost:
Node classification over bipartite graphs through projection. 37-87 - Dimitris Bertsimas, Agni Orfanoudaki, Holly M. Wiberg:
Interpretable clustering: an optimization approach. 89-138 - Dalibor Krleza, Boris Vrdoljak, Mario Brcic:
Statistical hierarchical clustering algorithm for outlier detection in evolving data streams. 139-184 - Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski:
Imputation of clinical covariates in time series. 185-248
Volume 110, Number 2, February 2021
- Dimitris Bertsimas, Bartolomeo Stellato:
The voice of optimization. 249-277 - Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer:
LoRAS: an oversampling approach for imbalanced datasets. 279-301 - Christina Heinze-Deml, Nicolai Meinshausen:
Conditional variance penalties and domain shift robustness. 303-348 - Yael Travis-Lumer, Yair Goldberg:
Kernel machines for current status data. 349-391 - Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of neural networks by enforcing Lipschitz continuity. 393-416 - Alberto Bemporad, Dario Piga:
Global optimization based on active preference learning with radial basis functions. 417-448 - Jinhong Jung, Lee Sael:
Correction to: Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach. 449
Volume 110, Number 3, March 2021
- David J. Hand, Peter Christen, Nishadi Kirielle:
F*: an interpretable transformation of the F-measure. 451-456 - Eyke Hüllermeier, Willem Waegeman:
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. 457-506 - Kishan Wimalawarne, Hiroshi Mamitsuka:
Reshaped tensor nuclear norms for higher order tensor completion. 507-531 - Dai Shi, Junbin Gao, Xia Hong, S. T. Boris Choy, Zhiyong Wang:
Coupling matrix manifolds assisted optimization for optimal transport problems. 533-558 - Prashanth L. A., Nathaniel Korda, Rémi Munos:
Concentration bounds for temporal difference learning with linear function approximation: the case of batch data and uniform sampling. 559-618 - Jinhong Jung, Lee Sael:
Correction to: Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach. 619-620
Volume 110, Number 4, April 2021
- Emily P. Hendryx, Beatrice M. Riviere, Craig G. Rusin:
An extended DEIM algorithm for subset selection and class identification. 621-650 - Jiyu Chen, Yiwen Guo, Qianjun Zheng, Hao Chen:
Protect privacy of deep classification networks by exploiting their generative power. 651-674 - Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber, Cédric Gouy-Pailler, Renaud Sirdey:
SPEED: secure, PrivatE, and efficient deep learning. 675-694 - Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and harmful explanatory machine learning. 695-721 - Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti, Evelina Lamma:
Probabilistic inductive constraint logic. 723-754 - Stassa Patsantzis, Stephen H. Muggleton:
Top program construction and reduction for polynomial time Meta-Interpretive learning. 755-778 - Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo:
AUTOMAT[R]IX: learning simple matrix pipelines. 779-799 - Andrew Cropper, Rolf Morel:
Learning programs by learning from failures. 801-856
Volume 110, Number 5, May 2021
- Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, Seungjin Choi:
Bayesian optimization with approximate set kernels. 857-879 - Luc Giffon, Valentin Emiya, Hachem Kadri, Liva Ralaivola:
QuicK-means: accelerating inference for K-means by learning fast transforms. 881-905 - Wei Wang, Bing Guo, Yan Shen, Han Yang, Yaosen Chen, Xinhua Suo:
Robust supervised topic models under label noise. 907-931 - Tomohiko Mizutani:
Convex programming based spectral clustering. 933-964 - Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias:
Large scale multi-label learning using Gaussian processes. 965-987 - Blaz Skrlj, Matej Martinc, Nada Lavrac, Senja Pollak:
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification. 989-1028 - Ximing Li, Yang Wang, Jihong Ouyang, Meng Wang:
Topic extraction from extremely short texts with variational manifold regularization. 1029-1066 - Daniel Andrade, Yuzuru Okajima:
Adaptive covariate acquisition for minimizing total cost of classification. 1067-1104
Volume 110, Number 6, June 2021
- Charles W. L. Gadd, Sara Wade, Akeel A. Shah:
Pseudo-marginal Bayesian inference for Gaussian process latent variable models. 1105-1143 - Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. 1145-1173 - Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. 1175-1197 - Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick:
Toward optimal probabilistic active learning using a Bayesian approach. 1199-1231 - Alihan Hüyük, Cem Tekin:
Multi-objective multi-armed bandit with lexicographically ordered and satisficing objectives. 1233-1266 - Josiah P. Hanna, Scott Niekum, Peter Stone:
Importance sampling in reinforcement learning with an estimated behavior policy. 1267-1317 - Yueqi Cao, Didong Li, Huafei Sun, Amir H. Assadi, Shiqiang Zhang:
Efficient Weingarten map and curvature estimation on manifolds. 1319-1344 - Jiye Liang, Junbiao Cui, Jie Wang, Wei Wei:
Graph-based semi-supervised learning via improving the quality of the graph dynamically. 1345-1388 - Javier Fernández, Luke Bornn, Daniel Cervone:
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. 1389-1427 - Rashid Bakirov, Damien Fay, Bogdan Gabrys:
Automated adaptation strategies for stream learning. 1429-1462 - Geoffrey A. Converse, Mariana Curi, Suely Oliveira, Jonathan Templin:
Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders. 1463-1480 - Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols, Asma Dachraoui:
Early classification of time series. 1481-1504 - Shaowei Wei, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang:
Multiple clusterings of heterogeneous information networks. 1505-1526 - Junjie Shi, Jiang Bian, Jakob Richter, Kuan-Hsun Chen, Jörg Rahnenführer, Haoyi Xiong, Jian-Jia Chen:
MODES: model-based optimization on distributed embedded systems. 1527-1547
Volume 110, Number 7, July 2021
- Giorgio Gnecco, Federico Nutarelli, Daniela Selvi:
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model. 1549-1584 - Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka:
Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels. 1585-1607 - Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig:
Incorporating symbolic domain knowledge into graph neural networks. 1609-1636 - Arnaud Nguembang Fadja, Fabrizio Riguzzi, Evelina Lamma:
Learning hierarchical probabilistic logic programs. 1637-1693 - Gustav Sourek, Filip Zelezný, Ondrej Kuzelka:
Beyond graph neural networks with lifted relational neural networks. 1695-1738 - Daniele Meli, Mohan Sridharan, Paolo Fiorini:
Inductive learning of answer set programs for autonomous surgical task planning. 1739-1763 - Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama:
Distance metric learning for graph structured data. 1765-1811 - Jiaoyan Chen, Pan Hu, Ernesto Jiménez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, Ian Horrocks:
OWL2Vec*: embedding of OWL ontologies. 1813-1845 - Varun Embar, Sriram Srinivasan, Lise Getoor:
A comparison of statistical relational learning and graph neural networks for aggregate graph queries. 1847-1866 - Hao Kong, Canyi Lu, Zhouchen Lin:
Tensor Q-rank: new data dependent definition of tensor rank. 1867-1900 - Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint optimization of an autoencoder for clustering and embedding. 1901-1937 - Quentin Klopfenstein, Samuel Vaiter:
Linear support vector regression with linear constraints. 1939-1974
Volume 110, Number 8, August 2021
- Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki:
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations. 1975-2003 - Wanli Shi, Bin Gu, Xiang Li, Cheng Deng, Heng Huang:
Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification. 2005-2033 - Rilwan A. Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta:
TRU-NET: a deep learning approach to high resolution prediction of rainfall. 2035-2062 - Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
On testing transitivity in online preference learning. 2063-2084 - Quoc-Tuan Truong, Hady W. Lauw:
Variational learning from implicit bandit feedback. 2085-2105 - David S. Watson, Marvin N. Wright:
Testing conditional independence in supervised learning algorithms. 2107-2129 - Haiyan Jiang, Haoyi Xiong, Dongrui Wu, Ji Liu, Dejing Dou:
AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow. 2131-2150 - Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban:
Sampled Gromov Wasserstein. 2151-2186 - Michael Steininger, Konstantin Kobs, Padraig Davidson, Anna Krause, Andreas Hotho:
Density-based weighting for imbalanced regression. 2187-2211 - Kai Chen, Twan van Laarhoven, Elena Marchiori:
Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting. 2213-2238 - Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo:
Information-theoretic regularization for learning global features by sequential VAE. 2239-2266 - Riad Akrour, Asma Atamna, Jan Peters:
Convex optimization with an interpolation-based projection and its application to deep learning. 2267-2289
Volume 110, Number 9, September 2021
- Yuxi Li, Alborz Geramifard, Lihong Li, Csaba Szepesvári, Tao Wang:
Guest editorial: special issue on reinforcement learning for real life. 2291-2293 - Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy:
Inverse reinforcement learning in contextual MDPs. 2295-2334 - Ioannis Boukas, Damien Ernst, Thibaut Théate, Adrien Bolland, Alexandre Huynen, Martin Buchwald, Christelle Wynants, Bertrand Cornélusse:
A deep reinforcement learning framework for continuous intraday market bidding. 2335-2387 - William Cai, Josh Grossman, Zhiyuan (Jerry) Lin, Hao Sheng, Johnny Tian-Zheng Wei, Joseph Jay Williams, Sharad Goel:
Bandit algorithms to personalize educational chatbots. 2389-2418 - Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester:
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. 2419-2468 - Josiah P. Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, Peter Stone:
Grounded action transformation for sim-to-real reinforcement learning. 2469-2499 - Srivatsan Krishnan, Behzad Boroujerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi:
Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation. 2501-2540 - Amarildo Likmeta, Alberto Maria Metelli, Giorgia Ramponi, Andrea Tirinzoni, Matteo Giuliani, Marcello Restelli:
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems. 2541-2576 - Scott Rome, Tianwen Chen, Michael Kreisel, Ding Zhou:
Lessons on off-policy methods from a notification component of a chatbot. 2577-2602 - Wenjie Shang, Qingyang Li, Zhiwei (Tony) Qin, Yang Yu, Yiping Meng, Jieping Ye:
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation. 2603-2640 - Julian Skirzynski, Frederic Becker, Falk Lieder:
Automatic discovery of interpretable planning strategies. 2641-2683 - Sabina Tomkins, Peng Liao, Predrag V. Klasnja, Susan A. Murphy:
IntelligentPooling: practical Thompson sampling for mHealth. 2685-2727
Volume 110, Number 10, October 2021
- Juan-Luis Suárez, Salvador García, Francisco Herrera:
Ordinal regression with explainable distance metric learning based on ordered sequences. 2729-2762 - Xiaomin Zhang, Xiaojin Zhu, Po-Ling Loh:
Provable training set debugging for linear regression. 2763-2834 - Shay Vargaftik, Isaac Keslassy, Ariel Orda, Yaniv Ben-Itzhak:
RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods. 2835-2866 - Xing Zhao, Manos Papagelis, Aijun An, Bao Xin Chen, Junfeng Liu, Yonggang Hu:
ZipLine: an optimized algorithm for the elastic bulk synchronous parallel model. 2867-2903 - Bo Kang, Dario García-García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie:
Conditional t-SNE: more informative t-SNE embeddings. 2905-2940 - Bhaskar Mukhoty, Subhajit Dutta, Purushottam Kar:
Robust non-parametric regression via incoherent subspace projections. 2941-2989 - Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann:
Introduction to the special issue of the ECML PKDD 2021 journal track. 2991-2992
Volume 110, Number 11, December 2021
- Cong Yang, Wenfeng Wang, Yunhui Zhang, Zhikai Zhang, Lina Shen, Yipeng Li, John See:
MLife: a lite framework for machine learning lifecycle initialization. 2993-3013 - Bartosz Krawczyk:
Tensor decision trees for continual learning from drifting data streams. 3015-3035 - José Pedro Pinto, André Pimenta, Paulo Novais:
Deep learning and multivariate time series for cheat detection in video games. 3037-3057 - Michal Koziarski, Colin Bellinger, Michal Wozniak:
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification. 3059-3093 - Alessio Benavoli, Dario Azzimonti, Dario Piga:
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes. 3095-3133 - Esteban G. Tabak, Giulio Trigila, Wenjun Zhao:
Data driven conditional optimal transport. 3135-3155 - Sofia Fernandes, Mário Antunes, Diogo Gomes, Rui L. Aguiar:
Misalignment problem in matrix decomposition with missing values. 3157-3175 - Dimitris Bertsimas, Jean Pauphilet, Bart P. G. Van Parys:
Sparse classification: a scalable discrete optimization perspective. 3177-3209 - Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony J. Bagnall:
HIVE-COTE 2.0: a new meta ensemble for time series classification. 3211-3243 - Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson:
Loss aware post-training quantization. 3245-3262
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