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Publication search results
found 102 matches
- 2021
- 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. Mach. Learn. 110(3): 559-618 (2021) - Youssef Achenchabe, Alexis Bondu, Antoine Cornuéjols, Asma Dachraoui:
Early classification of time series. Mach. Learn. 110(6): 1481-1504 (2021) - Rilwan A. Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta:
TRU-NET: a deep learning approach to high resolution prediction of rainfall. Mach. Learn. 110(8): 2035-2062 (2021) - Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and harmful explanatory machine learning. Mach. Learn. 110(4): 695-721 (2021) - Riad Akrour, Asma Atamna, Jan Peters:
Convex optimization with an interpolation-based projection and its application to deep learning. Mach. Learn. 110(8): 2267-2289 (2021) - Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo:
Information-theoretic regularization for learning global features by sequential VAE. Mach. Learn. 110(8): 2239-2266 (2021) - Daniel Andrade, Yuzuru Okajima:
Adaptive covariate acquisition for minimizing total cost of classification. Mach. Learn. 110(5): 1067-1104 (2021) - Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann:
Introduction to the special issue of the ECML PKDD 2021 journal track. Mach. Learn. 110(10): 2991-2992 (2021) - Rashid Bakirov, Damien Fay, Bogdan Gabrys:
Automated adaptation strategies for stream learning. Mach. Learn. 110(6): 1429-1462 (2021) - Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer:
LoRAS: an oversampling approach for imbalanced datasets. Mach. Learn. 110(2): 279-301 (2021) - Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy:
Inverse reinforcement learning in contextual MDPs. Mach. Learn. 110(9): 2295-2334 (2021) - Alberto Bemporad, Dario Piga:
Global optimization based on active preference learning with radial basis functions. Mach. Learn. 110(2): 417-448 (2021) - Alessio Benavoli, Dario Azzimonti, Dario Piga:
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes. Mach. Learn. 110(11): 3095-3133 (2021) - Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski:
Imputation of clinical covariates in time series. Mach. Learn. 110(1): 185-248 (2021) - Dimitris Bertsimas, Agni Orfanoudaki, Holly M. Wiberg:
Interpretable clustering: an optimization approach. Mach. Learn. 110(1): 89-138 (2021) - Dimitris Bertsimas, Jean Pauphilet, Bart P. G. Van Parys:
Sparse classification: a scalable discrete optimization perspective. Mach. Learn. 110(11): 3177-3209 (2021) - Dimitris Bertsimas, Bartolomeo Stellato:
The voice of optimization. Mach. Learn. 110(2): 249-277 (2021) - Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint optimization of an autoencoder for clustering and embedding. Mach. Learn. 110(7): 1901-1937 (2021) - 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. Mach. Learn. 110(9): 2335-2387 (2021) - William Cai, Josh Grossman, Zhiyuan (Jerry) Lin, Hao Sheng, Johnny Tian-Zheng Wei, Joseph Jay Williams, Sharad Goel:
Bandit algorithms to personalize educational chatbots. Mach. Learn. 110(9): 2389-2418 (2021) - Yueqi Cao, Didong Li, Huafei Sun, Amir H. Assadi, Shiqiang Zhang:
Efficient Weingarten map and curvature estimation on manifolds. Mach. Learn. 110(6): 1319-1344 (2021) - Jiyu Chen, Yiwen Guo, Qianjun Zheng, Hao Chen:
Protect privacy of deep classification networks by exploiting their generative power. Mach. Learn. 110(4): 651-674 (2021) - Jiaoyan Chen, Pan Hu, Ernesto Jiménez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, Ian Horrocks:
OWL2Vec*: embedding of OWL ontologies. Mach. Learn. 110(7): 1813-1845 (2021) - Kai Chen, Twan van Laarhoven, Elena Marchiori:
Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting. Mach. Learn. 110(8): 2213-2238 (2021) - Geoffrey A. Converse, Mariana Curi, Suely Oliveira, Jonathan Templin:
Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders. Mach. Learn. 110(6): 1463-1480 (2021) - Andrew Cropper, Rolf Morel:
Learning programs by learning from failures. Mach. Learn. 110(4): 801-856 (2021) - Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig:
Incorporating symbolic domain knowledge into graph neural networks. Mach. Learn. 110(7): 1609-1636 (2021) - 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. Mach. Learn. 110(9): 2419-2468 (2021) - Varun Embar, Sriram Srinivasan, Lise Getoor:
A comparison of statistical relational learning and graph neural networks for aggregate graph queries. Mach. Learn. 110(7): 1847-1866 (2021) - Arnaud Nguembang Fadja, Fabrizio Riguzzi, Evelina Lamma:
Learning hierarchical probabilistic logic programs. Mach. Learn. 110(7): 1637-1693 (2021)
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