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ORCIDMachine Learning, Volume 112
Volume 112, Number 1, January 2023
- Anton Björklund
, Jarmo Mäkelä, Kai Puolamäki:
SLISEMAP: supervised dimensionality reduction through local explanations. 1-43 - Fatoumata Dama, Christine Sinoquet:
Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting - application to machine health prognostics. 45-97 - Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda:
MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm. 99-129 - Niklas Åkerblom, Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani:
Online learning of network bottlenecks via minimax paths. 131-150 - Eugène Ndiaye, Ichiro Takeuchi:
Root-finding approaches for computing conformal prediction set. 151-176 - Juan Alvarado, Yuyi Wang, Jan Ramon:
Limits of multi-relational graphs. 177-216 - Viktor Bengs, Eyke Hüllermeier:
Multi-armed bandits with censored consumption of resources. 217-240 - Shota Saito, Mark Herbster:
Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm. 241-280 - Ling Luo, Bin Li, Xuhui Fan, Yang Wang, Irena Koprinska, Fang Chen:
Dynamic customer segmentation via hierarchical fragmentation-coagulation processes. 281-310 - Kunsheng Tang, Ping Li, Yide Song, Tian Luo:
Reconciling privacy and utility: an unscented Kalman filter-based framework for differentially private machine learning. 311-351 - Liang Mi, Azadeh Sheikholeslami, José Bento:
A family of pairwise multi-marginal optimal transports that define a generalized metric. 353-384
Volume 112, Number 2, February 2023
- Elena Battaglia, Ruggero G. Pensa:
A parameter-less algorithm for tensor co-clustering. 385-427 - Ariyan Bighashdel, Pavol Jancura, Gijs Dubbelman:
Correction to: Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations. 429-430 - Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan:
Constrained regret minimization for multi-criterion multi-armed bandits. 431-458 - Sofia Fernandes, Hadi Fanaee-T, João Gama, Leo Tisljaric, Tomislav Smuc:
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks. 459-481 - Emanuele Pesce, Giovanni Montana:
Learning multi-agent coordination through connectivity-driven communication. 483-514 - Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
FFNSL: Feed-Forward Neural-Symbolic Learner. 515-569 - Phuong Huynh Van Quoc, Johannes Fürnkranz, Florian Beck:
Efficient learning of large sets of locally optimal classification rules. 571-610 - Bahar Azari, Deniz Erdogmus:
Circular-symmetric correlation layer. 611-631 - Adnan Ahmad, Wei Luo, Antonio Robles-Kelly:
Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. 633-654 - Heinke Hihn, Daniel A. Braun:
Hierarchically structured task-agnostic continual learning. 655-686 - Alexis Cvetkov-Iliev, Alexandre Allauzen, Gaël Varoquaux:
Relational data embeddings for feature enrichment with background information. 687-720 - Pavlin G. Policar, Martin Strazar, Blaz Zupan:
Embedding to reference t-SNE space addresses batch effects in single-cell classification. 721-740
Volume 112, Number 3, March 2023
- Vincent Grari, Sylvain Lamprier, Marcin Detyniecki:
Adversarial learning for counterfactual fairness. 741-763 - Luiz Angelo Steffenel, Vagner Anabor, Damaris Kirsch Pinheiro, Lissette Guzman, Gabriela Dornelles Bittencourt, Hassan Bencherif:
Forecasting upper atmospheric scalars advection using deep learning: an O3 experiment. 765-788 - Tianyi Luo, Yang Liu:
Machine truth serum: a surprisingly popular approach to improving ensemble methods. 789-815 - David M. Bossens, Nicholas Bishop:
Explicit Explore, Exploit, or Escape (E4): near-optimal safety-constrained reinforcement learning in polynomial time. 817-858 - Qisong Yang, Thiago D. Simão, Simon H. Tindemans, Matthijs T. J. Spaan:
Safety-constrained reinforcement learning with a distributional safety critic. 859-887 - Shota Nakajima, Masashi Sugiyama:
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. 889-919 - Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Álvaro Moreno-Martínez, Gustau Camps-Valls:
Inference over radiative transfer models using variational and expectation maximization methods. 921-937 - Preethi Lahoti, P. Krishna Gummadi, Gerhard Weikum:
Responsible model deployment via model-agnostic uncertainty learning. 939-970 - Michael Thomas Smith, Kathrin Grosse, Michael Backes, Mauricio A. Álvarez:
Adversarial vulnerability bounds for Gaussian process classification. 971-1009 - Nan Xia, Hang Yu, Yin Wang, Junyu Xuan, Xiangfeng Luo:
DAFS: a domain aware few shot generative model for event detection. 1011-1031 - Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma:
Troubleshooting image segmentation models with human-in-the-loop. 1033-1051 - Jingzheng Li, Hailong Sun, Jiyi Li:
Beyond confusion matrix: learning from multiple annotators with awareness of instance features. 1053-1075
Volume 112, Number 4, April 2023
- Chunchao Ma, Mauricio A. Álvarez:
Large scale multi-output multi-class classification using Gaussian processes. 1077-1106 - Mahmoud Al Najar, Gregoire Thoumyre, Erwin W. J. Bergsma, Rafael Almar, Rachid Benshila, Dennis G. Wilson:
Satellite derived bathymetry using deep learning. 1107-1130 - Felix Mohr, Marcel Wever:
Naive automated machine learning. 1131-1170 - Mario Luca Bernardi, Marta Cimitile, Fabrizio Maria Maggi:
Data-aware process discovery for malware detection: an empirical study. 1171-1199 - Hadeer M. Sayed, Hesham E. ElDeeb, Shereen A. Taie:
Bimodal variational autoencoder for audiovisual speech recognition. 1201-1226 - Yantao Wei, Shujian Yu, Luis G. Sánchez Giraldo, José C. Príncipe:
Multiscale principle of relevant information for hyperspectral image classification. 1227-1252 - Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier:
Algorithm selection on a meta level. 1253-1286 - Georgios Makridis, Philip Mavrepis, Dimosthenis Kyriazis:
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety. 1287-1313 - Babatounde Moctard Oloulade, Jianliang Gao, Jiamin Chen, Raeed Al-Sabri, Tengfei Lyu:
Neural predictor-based automated graph classifier framework. 1315-1335 - Jure Brence, Jovan Tanevski, Jennifer Adams, Edward Malina, Saso Dzeroski:
Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations. 1337-1363 - Oghenejokpeme I. Orhobor, Nastasiya F. Grinberg, Larisa N. Soldatova, Ross D. King:
Imbalanced regression using regressor-classifier ensembles. 1365-1387 - Giovanni De Toni, Bruno Lepri, Andrea Passerini:
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis. 1389-1409
Volume 112, Number 5, May 2023
- Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon:
Correlated product of experts for sparse Gaussian process regression. 1411-1432 - Ziyi Chen, Bhavya Kailkhura, Yi Zhou:
An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization. 1433-1463 - Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting:
αILP: thinking visual scenes as differentiable logic programs. 1465-1497 - Chien-Min Yu, Ming-Hsin Chen, Hsuan-Tien Lin:
Learning key steps to attack deep reinforcement learning agents. 1499-1522 - Clément Lejeune, Josiane Mothe, Adil Soubki, Olivier Teste:
Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning. 1523-1549 - Céline Hocquette, Andrew Cropper:
Learning programs with magic values. 1551-1595 - Yanghao Zhang, Wenjie Ruan, Fu Wang, Xiaowei Huang:
Generalizing universal adversarial perturbations for deep neural networks. 1597-1626 - Xuhong Li, Haoyi Xiong, Siyu Huang, Shilei Ji, Dejing Dou:
Cross-model consensus of explanations and beyond for image classification models: an empirical study. 1627-1662 - Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong, Marya Bazzi, Mihai Cucuringu:
Local2Global: a distributed approach for scaling representation learning on graphs. 1663-1692 - Andrew James Turner, Ata Kabán:
PAC-learning with approximate predictors. 1693-1732 - Hugo N. Oliveira, Caio C. V. da Silva, Gabriel L. S. Machado, Keiller Nogueira, Jefersson A. dos Santos:
Fully convolutional open set segmentation. 1733-1784 - Zongyu Yin, Federico Reuben, Susan Stepney, Tom Collins:
Deep learning's shallow gains: a comparative evaluation of algorithms for automatic music generation. 1785-1822
Volume 112, Number 6, June 2023
- Ye Shi, Shao-Yuan Li, Sheng-Jun Huang:
Learning from crowds with sparse and imbalanced annotations. 1823-1845 - Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu:
A viable framework for semi-supervised learning on realistic dataset. 1847-1869 - Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin:
An instance-dependent simulation framework for learning with label noise. 1871-1896 - Bilge Celik, Prabhant Singh, Joaquin Vanschoren:
Online AutoML: an adaptive AutoML framework for online learning. 1897-1921 - Suncheng Xiang, Yuzhuo Fu, Mengyuan Guan, Ting Liu:
Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. 1923-1940 - Benjamin Lucas, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb, François Petitjean:
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping. 1941-1973 - Pierre Gloaguen, Laetitia Chapel, Chloé Friguet, Romain Tavenard:
Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data. 1975-2001 - Dorian Cazau, Paul Nguyen Hong Duc, J.-N. Druon, S. Matwins, Ronan Fablet:
Multimodal deep learning for cetacean distribution modeling of fin whales (Balaenoptera physalus) in the western Mediterranean Sea. 2003-2024 - Morteza Haghir Chehreghani:
Shift of pairwise similarities for data clustering. 2025-2051 - Gonzalo Jaimovitch-López, Cèsar Ferri, José Hernández-Orallo, Fernando Martínez-Plumed, María José Ramírez-Quintana:
Can language models automate data wrangling? 2053-2082 - Vítor Cerqueira, Luís Torgo, Paula Branco, Colin Bellinger:
Automated imbalanced classification via layered learning. 2083-2104 - Zhuorong Li, Daiwei Yu, Minghui Wu, Canghong Jin, Hongchuan Yu:
Adversarial supervised contrastive learning. 2105-2130 - Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala:
Improving fairness generalization through a sample-robust optimization method. 2131-2192 - Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, Dejing Dou:
Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models. 2193-2209 - Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau:
Weakly supervised change detection using guided anisotropic diffusion. 2211-2237
Volume 112, Number 7, July 2023
- Lei Zhou, Yang Liu, Pengcheng Zhang, Xiao Bai, Lin Gu, Jun Zhou, Yazhou Yao, Tatsuya Harada, Jin Zheng, Edwin R. Hancock:
Information bottleneck and selective noise supervision for zero-shot learning. 2239-2261 - Ariyan Bighashdel, Pavol Jancura, Gijs Dubbelman:
Model-free inverse reinforcement learning with multi-intention, unlabeled, and overlapping demonstrations. 2263-2296 - Oliver Struckmeier, Kshitij Tiwari, Ville Kyrki:
Autoencoding slow representations for semi-supervised data-efficient regression. 2297-2315 - Shalev Shaer, Yaniv Romano:
Learning to increase the power of conditional randomization tests. 2317-2357 - Cuong Manh Nguyen, Arun Raja, Le Zhang, Xun Xu, Balagopal Unnikrishnan, Mohamed Ragab, Kangkang Lu, Chuan-Sheng Foo:
Diverse and consistent multi-view networks for semi-supervised regression. 2359-2395 - Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen:
Unsupervised discretization by two-dimensional MDL-based histogram. 2397-2431 - Harshat Kumar, Alec Koppel, Alejandro Ribeiro:
On the sample complexity of actor-critic method for reinforcement learning with function approximation. 2433-2467 - Abhishake Rastogi, Peter Mathé:
Inverse learning in Hilbert scales. 2469-2499 - Mieczyslaw Alojzy Klopotek, Robert Albert Klopotek:
On the Discrepancy between Kleinberg's Clustering Axioms and k-Means Clustering Algorithm Behavior. 2501-2553 - Rakshitha Godahewa, Geoffrey I. Webb, Daniel F. Schmidt, Christoph Bergmeir:
SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. 2555-2591 - Mattijs Baert, Sam Leroux, Pieter Simoens:
Inverse reinforcement learning through logic constraint inference. 2593-2618 - Leonardo Teixeira, Brian Jalaian, Bruno Ribeiro:
Reducing classifier overconfidence against adversaries through graph algorithms. 2619-2651 - Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low:
Pruning during training by network efficacy modeling. 2653-2684 - Anthony Sicilia, Xingchen Zhao, Seong Jae Hwang:
Domain adversarial neural networks for domain generalization: when it works and how to improve. 2685-2721 - Naimin Jing, Ethan X. Fang, Cheng Yong Tang:
Robust matrix estimations meet Frank-Wolfe algorithm. 2723-2760
Volume 112, Number 8, August 2023
- Maryam Badar, Wolfgang Nejdl, Marco Fisichella:
FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. 2761-2786 - Ransalu Senanayake, Daniel J. Fremont, Mykel J. Kochenderfer, Alessio R. Lomuscio, Dragos D. Margineantu, Cheng Soon Ong:
Guest Editorial: Special issue on robust machine learning. 2787-2789 - Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian:
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. 2791-2819 - Taisuke Sato, Katsumi Inoue:
Differentiable learning of matricized DNFs and its application to Boolean networks. 2821-2843 - Dai Hai Nguyen, Tetsuya Sakurai:
Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains. 2845-2869 - Lei Tan, Shutong Wu, Wenxing Zhou, Xiaolin Huang:
Weighted neural tangent kernel: a generalized and improved network-induced kernel. 2871-2901 - Sydney M. Katz, Kyle D. Julian, Christopher A. Strong, Mykel J. Kochenderfer:
Generating probabilistic safety guarantees for neural network controllers. 2903-2931 - Matthew D. Norton, Johannes O. Royset:
Diametrical Risk Minimization: theory and computations. 2933-2951 - Hossein Askari, Yasir Latif, Hongfu Sun:
MapFlow: latent transition via normalizing flow for unsupervised domain adaptation. 2953-2974 - Shuisheng Zhou, Wendi Zhou:
Unified SVM algorithm based on LS-DC loss. 2975-3002 - Mohammad Azizmalayeri, Mohammad Hossein Rohban:
Lagrangian objective function leads to improved unforeseen attack generalization. 3003-3031 - Marco Loog, Jesse H. Krijthe, Manuele Bicego:
Also for k-means: more data does not imply better performance. 3033-3050
Volume 112, Number 9, September 2023
- Shuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito:
FairSwiRL: fair semi-supervised classification with representation learning. 3051-3076 - Bin Gu, Chenkang Zhang, Zhouyuan Huo, Heng Huang:
A new large-scale learning algorithm for generalized additive models. 3077-3104 - Matías Vera, Leonardo Rey Vega, Pablo Piantanida:
The role of mutual information in variational classifiers. 3105-3150 - David Schnörr, Christoph Schnörr:
Learning system parameters from turing patterns. 3151-3190 - Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Sanjay Chawla:
A3T: accuracy aware adversarial training. 3191-3210 - Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier calibration: a survey on how to assess and improve predicted class probabilities. 3211-3260 - Eleonora Giunchiglia, Mihaela Catalina Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz:
ROAD-R: the autonomous driving dataset with logical requirements. 3261-3291 - Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen:
Refining neural network predictions using background knowledge. 3293-3331 - Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana:
Considerations when learning additive explanations for black-box models. 3333-3359 - Chunming Zhang, Lixing Zhu, Yanbo Shen:
Robust estimation in regression and classification methods for large dimensional data. 3361-3411 - Fernando E. Casado, Dylan Lema, Roberto Iglesias, Carlos Vázquez Regueiro, Senén Barro:
Ensemble and continual federated learning for classification tasks. 3413-3453 - Alexander Brenning:
Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification. 3455-3471 - Jingzheng Li, Hailong Sun:
NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation. 3473-3496 - Adam White, Kwun Ho Ngan, James Phelan, Kevin Ryan, Saman Sadeghi Afgeh, Constantino Carlos Reyes-Aldasoro, Artur S. d'Avila Garcez:
Contrastive counterfactual visual explanations with overdetermination. 3497-3525 - Yian Deng, Tingting Mu:
Faster Riemannian Newton-type optimization by subsampling and cubic regularization. 3527-3589
Volume 112, Number 10, October 2023
- Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. 3591-3632