Machine Learning 2023: all accepted papers
- Parameter identifiability of a deep feedforward ReLU neural network ↗Joachim Bona-Pellissier, François Bachoc, François Malgouyres6 citations
- Ranking-preserved generative label enhancement ↗Yunan Lu, Weiwei Li, Huaxiong Li et al.6 citations
- Trajectory test-train overlap in next-location prediction datasets ↗Massimiliano Luca, Luca Pappalardo, Bruno Lepri et al.6 citations
- A new large-scale learning algorithm for generalized additive models ↗Bin Gu, Chenkang Zhang, Zhouyuan Huo et al.5 citations
- Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks ↗Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas et al.5 citations
- Continual variational dropout: a view of auxiliary local variables in continual learning ↗Nam Le Hai, Trang Thi Hong Nguyen, Linh Ngo Van et al.5 citations
- Deep intrinsically motivated exploration in continuous control ↗Baturay Sağlam, Süleyman S. Kozat5 citations
- Knowledge-aware image understanding with multi-level visual representation enhancement for visual question answering ↗Feng Yan, Zhe Li, Wushour Silamu et al.5 citations
- Optimizing model-agnostic random subspace ensembles ↗Vân Anh Huynh‐Thu, Pierre Geurts5 citations
- ProtoSimi: label correction for fine-grained visual categorization ↗Jialiang Shen, Yu Kai Yao, Shaoli Huang et al.5 citations
- Pruning during training by network efficacy modeling ↗Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low5 citations
- Robust matrix estimations meet Frank–Wolfe algorithm ↗Naimin Jing, Ethan X. Fang, Cheng Yong Tang5 citations
- Unsupervised discretization by two-dimensional MDL-based histogram ↗Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen5 citations
- Also for k-means: more data does not imply better performance ↗Marco Loog, Jesse H. Krijthe, Manuele Bicego4 citations
- An effective negative sampling approach for contrastive learning of sentence embedding ↗Qitao Tan, Xiaoying Song, Guanghui Ye et al.4 citations
- Autoencoding slow representations for semi-supervised data-efficient regression ↗Oliver Struckmeier, Kshitij Tiwari, Ville Kyrki4 citations
- Boundary-restricted metric learning ↗Shuo Chen, Chen Gong, Xiang Li et al.4 citations
- Discordance minimization-based imputation algorithms for missing values in rating data ↗Young Woong Park, Jinhak Kim, Dan Zhu4 citations
- Efficient zeroth-order proximal stochastic method for nonconvex nonsmooth black-box problems ↗Ehsan Kazemi, Liqiang Wang4 citations
- Heterogeneous multi-task feature learning with mixed \ell _{2,1} regularization ↗Yuan Zhong, Wei Xu, Xin Gao4 citations
- Inverse learning in Hilbert scales ↗Abhishake Rastogi, Peter Mathé4 citations
- Inverse reinforcement learning through logic constraint inference ↗Mattijs Baert, Sam Leroux, Pieter Simoens4 citations
- Learning logic programs by explaining their failures ↗Rolf Morel, Andrew Cropper4 citations
- Meta-classifier free negative sampling for extreme multilabel classification ↗Mohammadreza Qaraei, Rohit Babbar4 citations
- NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation ↗Jingzheng Li, Hailong Sun4 citations
- Softmin discrete minimax classifier for imbalanced classes and prior probability shifts ↗Cyprien Gilet, Marie Guyomard, Sébastien Destercke et al.4 citations
- Subspace Adaptation Prior for Few-Shot Learning ↗Mike Huisman, Aske Plaat, Jan N. van Rijn4 citations
- Bayesian tensor factorisations for time series of counts ↗Zhongzhen Wang, Πέτρος Δελλαπόρτας, Ioannis Kosmidis3 citations
- Composition of relational features with an application to explaining black-box predictors ↗Ashwin Srinivasan, A. Baskar, Tirtharaj Dash et al.3 citations
- Differentiable learning of matricized DNFs and its application to Boolean networks ↗Taisuke Sato, Katsumi Inoue3 citations
- Explainable models via compression of tree ensembles ↗Siwen Yan, Sriraam Natarajan, Saket Joshi et al.3 citations
- FairSwiRL: fair semi-supervised classification with representation learning ↗Shuyi Yang, Mattia Cerrato, Dino Ienco et al.3 citations
- Faster Riemannian Newton-type optimization by subsampling and cubic regularization ↗Yian Deng, Tingting Mu3 citations
- Feature extractor stacking for cross-domain few-shot learning ↗Hongyu Wang, Eibe Frank, Bernhard Pfahringer et al.3 citations
- Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM ↗Vidyadhar Jinnappa Aski, Rugved Sanjay Chavan, Vijaypal Singh Dhaka et al.3 citations
- GVFs in the real world: making predictions online for water treatment ↗Muhammad Kamran Janjua, Haseeb Shah, Martha White et al.3 citations
- Heterogeneous multi-task Gaussian Cox processes ↗Feng Zhou, Quyu Kong, Zhijie Deng et al.3 citations
- Learning programs with magic values ↗Céline Hocquette, Andrew Cropper3 citations
- Learning to bid and rank together in recommendation systems ↗Geng Ji, Wentao Jiang, Jiang Li et al.3 citations
- Machine learning from casual conversation ↗Awrad E. Mohammed Ali, Avelino J. González3 citations
- MapFlow: latent transition via normalizing flow for unsupervised domain adaptation ↗Hossein Askari, Yasir Latif, Hongfu Sun3 citations
- On the incompatibility of accuracy and equal opportunity ↗Carlos Pinzón, Catuscia Palamidessi A, Pablo Piantanida et al.3 citations
- Robust estimation in regression and classification methods for large dimensional data ↗Chunming Zhang, Lixing Zhu, Yanbo Shen3 citations
- Tensor completion with noisy side information ↗Dimitris Bertsimas, Colin Pawlowski3 citations
- Upper and lower bounds for complete linkage in general metric spaces ↗Anna Arutyunova, Anna Großwendt, Heiko Röglin et al.3 citations
- logicDT: a procedure for identifying response-associated interactions between binary predictors ↗Michael Lau, Tamara Schikowski, Holger R. Schwender3 citations
- A general framework for the practical disintegration of PAC-Bayesian bounds ↗Paul Viallard, Pascal Germain, Amaury Habrard et al.2 citations
- Active learning algorithm through the lens of rejection arguments ↗Christophe Denis, Mohamed Hebiri, Boris Edgar Ndjia Njike et al.2 citations
- Classification Algorithms ↗T V Geetha, Selvaraju Sendhilkumar2 citations
- Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning ↗Clément Lejeune, Josiane Mothe, Adil Soubki et al.2 citations
- Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events ↗Lucas P. Damasceno, Egzona Rexhepi, Allison Shafer et al.2 citations
- Introduction ↗T V Geetha, Selvaraju Sendhilkumar2 citations
- Lagrangian objective function leads to improved unforeseen attack generalization ↗Mohammad Azizmalayeri, Mohammad Hossein Rohban2 citations
- Local2Global: a distributed approach for scaling representation learning on graphs ↗Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong et al.2 citations
- Markov chain importance sampling for minibatches ↗Cheng–Der Fuh, Chuan‐Ju Wang, Chen-Hung Pai2 citations
- Modeling PU learning using probabilistic logic programming ↗Victor Verreet, Luc De Raedt, Jessa Bekker2 citations
- Neural network structure simplification by assessing evolution in node weight magnitude ↗Ralf Riedel, Aviv Segev2 citations
- Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity ↗Evangelos Michelioudakis, Alexander Artikis, Γεώργιος Παλιούρας2 citations
- Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment ↗Théo Verhelst, Denis Mercier, Jeevan Shrestha et al.2 citations
- Sanitized clustering against confounding bias ↗Yinghua Yao, Yuangang Pan, Jing Li et al.2 citations
- Scalable variable selection for two-view learning tasks with projection operators ↗Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le et al.2 citations
- The role of mutual information in variational classifiers ↗Matías Vera, Leonardo J. Rey Vega, Pablo Piantanida2 citations
- Weighted neural tangent kernel: a generalized and improved network-induced kernel ↗Lei Tan, Shutong Wu, Wenxing Zhou et al.2 citations
- An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization ↗Ziyi Chen, Bhavya Kailkhura, Yi Zhou1 citation
- Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning ↗Lingwei Zhu, Takamitsu Matsubara1 citation
- Deep doubly robust outcome weighted learning ↗Xiaotong Jiang, Xin Zhou, Michael R. Kosorok1 citation
- Dense subgraphs induced by edge labels ↗Iiro Kumpulainen, Nikolaj Tatti1 citation
- Diverse and consistent multi-view networks for semi-supervised regression ↗Cuong Nguyen, Arun Raja, Le Zhang et al.1 citation
- Heuristic search of optimal machine teaching curricula ↗Manuel García Piqueras, José Hernández‐Orallo1 citation
- Machine Learning Applications ↗T V Geetha, Selvaraju Sendhilkumar1 citation
- Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains ↗Hai Nguyen, Tetsuya Sakurai1 citation
- PAC-learning with approximate predictors ↗Andrew James Turner, Ata Kabán1 citation
- Scale-preserving automatic concept extraction (SPACE) ↗Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander et al.1 citation
- Towards accurate knowledge transfer via target-awareness representation disentanglement ↗Xingjian Li, Xingjian Li, Di Hu et al.1 citation
- Answers to exercises ↗Marco Gori, Alessandro Betti, Stefano Melacci
- Biquality learning: a framework to design algorithms dealing with closed-set distribution shifts ↗Pierre Nodet, Vincent Lemaire, Alexis Bondu et al.
- Deep architectures ↗Marco Gori, Alessandro Betti, Stefano Melacci
- Domain Based Machine Learning Applications ↗T V Geetha, Selvaraju Sendhilkumar
- Epilogue ↗Marco Gori, Alessandro Betti, Stefano Melacci
- Ethical Aspects of Machine Learning ↗T V Geetha, Selvaraju Sendhilkumar
- Foundations and Categories of Machine Learning Techniques ↗T V Geetha, Selvaraju Sendhilkumar
- Introduction to Deep Learning and Convolutional Neural Networks ↗T V Geetha, Selvaraju Sendhilkumar
- Kernel machines ↗Marco Gori, Alessandro Betti, Stefano Melacci
- Learning to increase the power of conditional randomization tests ↗Shalev Shaer, Yaniv Romano
- Linear threshold machines ↗Marco Gori, Alessandro Betti, Stefano Melacci
- Machine Learning: Tools and Software ↗T V Geetha, Selvaraju Sendhilkumar
- Mathematical Foundations and Machine Learning ↗T V Geetha, Selvaraju Sendhilkumar
- Online binary classification from similar and dissimilar data ↗Senlin Shu, Haobo Wang, Zhuowei Wang et al.
- Other Models of Deep Learning and Applications of Deep Learning ↗T V Geetha, Selvaraju Sendhilkumar
- Performance Evaluation and Ensemble Methods ↗T V Geetha, Selvaraju Sendhilkumar
- Probabilistic and Regression Based Approaches ↗T V Geetha, Selvaraju Sendhilkumar
- Reducing classifier overconfidence against adversaries through graph algorithms ↗Leonardo Vilela Teixeira, Brian A. Jalaian, Bruno Ribeiro
- Reinforcement Learning ↗T V Geetha, Selvaraju Sendhilkumar
- Scale-preserving automatic concept extraction (SPACE) ↗Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander et al.
- Sequence Models ↗T V Geetha, Selvaraju Sendhilkumar
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