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title: "Machine Learning 2023 — all accepted papers, page 2"
description: "All 203 papers of Machine Learning 2023 · page 2 of 3, the most cited first; those with our summary link to it."
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# Machine Learning 2023: all accepted papers

203 papers · page 2 of 3 · the most cited first · [← Machine Learning 2023](https://md.research.elamur.ai/free/en/venues/machine-learning/2023/)

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101. **[Parameter identifiability of a deep feedforward ReLU neural network ↗](https://doi.org/10.1007/s10994-023-06355-4)**Joachim Bona-Pellissier, François Bachoc, François Malgouyres*6 citations*
102. **[Ranking-preserved generative label enhancement ↗](https://doi.org/10.1007/s10994-023-06388-9)**Yunan Lu, Weiwei Li, Huaxiong Li et al.*6 citations*
103. **[Trajectory test-train overlap in next-location prediction datasets ↗](https://doi.org/10.1007/s10994-023-06386-x)**Massimiliano Luca, Luca Pappalardo, Bruno Lepri et al.*6 citations*
104. **[A new large-scale learning algorithm for generalized additive models ↗](https://doi.org/10.1007/s10994-023-06339-4)**Bin Gu, Chenkang Zhang, Zhouyuan Huo et al.*5 citations*
105. **[Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks ↗](https://doi.org/10.1007/s10994-023-06359-0)**Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas et al.*5 citations*
106. **[Continual variational dropout: a view of auxiliary local variables in continual learning ↗](https://doi.org/10.1007/s10994-023-06487-7)**Nam Le Hai, Trang Thi Hong Nguyen, Linh Ngo Van et al.*5 citations*
107. **[Deep intrinsically motivated exploration in continuous control ↗](https://doi.org/10.1007/s10994-023-06363-4)**Baturay Sağlam, Süleyman S. Kozat*5 citations*
108. **[Knowledge-aware image understanding with multi-level visual representation enhancement for visual question answering ↗](https://doi.org/10.1007/s10994-023-06426-6)**Feng Yan, Zhe Li, Wushour Silamu et al.*5 citations*
109. **[Optimizing model-agnostic random subspace ensembles ↗](https://doi.org/10.1007/s10994-023-06427-5)**Vân Anh Huynh‐Thu, Pierre Geurts*5 citations*
110. **[ProtoSimi: label correction for fine-grained visual categorization ↗](https://doi.org/10.1007/s10994-023-06313-0)**Jialiang Shen, Yu Kai Yao, Shaoli Huang et al.*5 citations*
111. **[Pruning during training by network efficacy modeling ↗](https://doi.org/10.1007/s10994-023-06304-1)**Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low*5 citations*
112. **[Robust matrix estimations meet Frank–Wolfe algorithm ↗](https://doi.org/10.1007/s10994-023-06325-w)**Naimin Jing, Ethan X. Fang, Cheng Yong Tang*5 citations*
113. **[Unsupervised discretization by two-dimensional MDL-based histogram ↗](https://doi.org/10.1007/s10994-022-06294-6)**Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen*5 citations*
114. **[Also for k-means: more data does not imply better performance ↗](https://doi.org/10.1007/s10994-023-06361-6)**Marco Loog, Jesse H. Krijthe, Manuele Bicego*4 citations*
115. **[An effective negative sampling approach for contrastive learning of sentence embedding ↗](https://doi.org/10.1007/s10994-023-06408-8)**Qitao Tan, Xiaoying Song, Guanghui Ye et al.*4 citations*
116. **[Autoencoding slow representations for semi-supervised data-efficient regression ↗](https://doi.org/10.1007/s10994-022-06299-1)**Oliver Struckmeier, Kshitij Tiwari, Ville Kyrki*4 citations*
117. **[Boundary-restricted metric learning ↗](https://doi.org/10.1007/s10994-023-06380-3)**Shuo Chen, Chen Gong, Xiang Li et al.*4 citations*
118. **[Discordance minimization-based imputation algorithms for missing values in rating data ↗](https://doi.org/10.1007/s10994-023-06452-4)**Young Woong Park, Jinhak Kim, Dan Zhu*4 citations*
119. **[Efficient zeroth-order proximal stochastic method for nonconvex nonsmooth black-box problems ↗](https://doi.org/10.1007/s10994-023-06409-7)**Ehsan Kazemi, Liqiang Wang*4 citations*
120. **[Heterogeneous multi-task feature learning with mixed \ell _{2,1} regularization ↗](https://doi.org/10.1007/s10994-023-06410-0)**Yuan Zhong, Wei Xu, Xin Gao*4 citations*
121. **[Inverse learning in Hilbert scales ↗](https://doi.org/10.1007/s10994-022-06284-8)**Abhishake Rastogi, Peter Mathé*4 citations*
122. **[Inverse reinforcement learning through logic constraint inference ↗](https://doi.org/10.1007/s10994-023-06311-2)**Mattijs Baert, Sam Leroux, Pieter Simoens*4 citations*
123. **[Learning logic programs by explaining their failures ↗](https://doi.org/10.1007/s10994-023-06358-1)**Rolf Morel, Andrew Cropper*4 citations*
124. **[Meta-classifier free negative sampling for extreme multilabel classification ↗](https://doi.org/10.1007/s10994-023-06468-w)**Mohammadreza Qaraei, Rohit Babbar*4 citations*
125. **[NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation ↗](https://doi.org/10.1007/s10994-023-06343-8)**Jingzheng Li, Hailong Sun*4 citations*
126. **[Softmin discrete minimax classifier for imbalanced classes and prior probability shifts ↗](https://doi.org/10.1007/s10994-023-06397-8)**Cyprien Gilet, Marie Guyomard, Sébastien Destercke et al.*4 citations*
127. **[Subspace Adaptation Prior for Few-Shot Learning ↗](https://doi.org/10.1007/s10994-023-06393-y)**Mike Huisman, Aske Plaat, Jan N. van Rijn*4 citations*
128. **[Bayesian tensor factorisations for time series of counts ↗](https://doi.org/10.1007/s10994-023-06441-7)**Zhongzhen Wang, Πέτρος Δελλαπόρτας, Ioannis Kosmidis*3 citations*
129. **[Composition of relational features with an application to explaining black-box predictors ↗](https://doi.org/10.1007/s10994-023-06399-6)**Ashwin Srinivasan, A. Baskar, Tirtharaj Dash et al.*3 citations*
130. **[Differentiable learning of matricized DNFs and its application to Boolean networks ↗](https://doi.org/10.1007/s10994-023-06346-5)**Taisuke Sato, Katsumi Inoue*3 citations*
131. **[Explainable models via compression of tree ensembles ↗](https://doi.org/10.1007/s10994-023-06463-1)**Siwen Yan, Sriraam Natarajan, Saket Joshi et al.*3 citations*
132. **[FairSwiRL: fair semi-supervised classification with representation learning ↗](https://doi.org/10.1007/s10994-023-06342-9)**Shuyi Yang, Mattia Cerrato, Dino Ienco et al.*3 citations*
133. **[Faster Riemannian Newton-type optimization by subsampling and cubic regularization ↗](https://doi.org/10.1007/s10994-023-06321-0)**Yian Deng, Tingting Mu*3 citations*
134. **[Feature extractor stacking for cross-domain few-shot learning ↗](https://doi.org/10.1007/s10994-023-06483-x)**Hongyu Wang, Eibe Frank, Bernhard Pfahringer et al.*3 citations*
135. **[Forecasting of mobile network traffic and spatio–temporal analysis using modLSTM ↗](https://doi.org/10.1007/s10994-023-06471-1)**Vidyadhar Jinnappa Aski, Rugved Sanjay Chavan, Vijaypal Singh Dhaka et al.*3 citations*
136. **[GVFs in the real world: making predictions online for water treatment ↗](https://doi.org/10.1007/s10994-023-06413-x)**Muhammad Kamran Janjua, Haseeb Shah, Martha White et al.*3 citations*
137. **[Heterogeneous multi-task Gaussian Cox processes ↗](https://doi.org/10.1007/s10994-023-06382-1)**Feng Zhou, Quyu Kong, Zhijie Deng et al.*3 citations*
138. **[Learning programs with magic values ↗](http://dx.doi.org/10.1007/s10994-022-06274-w)**Céline Hocquette, Andrew Cropper*3 citations*
139. **[Learning to bid and rank together in recommendation systems ↗](https://doi.org/10.1007/s10994-023-06444-4)**Geng Ji, Wentao Jiang, Jiang Li et al.*3 citations*
140. **[Machine learning from casual conversation ↗](https://doi.org/10.1007/s10994-023-06383-0)**Awrad E. Mohammed Ali, Avelino J. González*3 citations*
141. **[MapFlow: latent transition via normalizing flow for unsupervised domain adaptation ↗](https://doi.org/10.1007/s10994-023-06357-2)**Hossein Askari, Yasir Latif, Hongfu Sun*3 citations*
142. **[On the incompatibility of accuracy and equal opportunity ↗](https://doi.org/10.1007/s10994-023-06331-y)**Carlos Pinzón, Catuscia Palamidessi A, Pablo Piantanida et al.*3 citations*
143. **[Robust estimation in regression and classification methods for large dimensional data ↗](https://doi.org/10.1007/s10994-023-06349-2)**Chunming Zhang, Lixing Zhu, Yanbo Shen*3 citations*
144. **[Tensor completion with noisy side information ↗](https://doi.org/10.1007/s10994-023-06338-5)**Dimitris Bertsimas, Colin Pawlowski*3 citations*
145. **[Upper and lower bounds for complete linkage in general metric spaces ↗](https://doi.org/10.1007/s10994-023-06486-8)**Anna Arutyunova, Anna Großwendt, Heiko Röglin et al.*3 citations*
146. **[logicDT: a procedure for identifying response-associated interactions between binary predictors ↗](https://doi.org/10.1007/s10994-023-06488-6)**Michael Lau, Tamara Schikowski, Holger R. Schwender*3 citations*
147. **[A general framework for the practical disintegration of PAC-Bayesian bounds ↗](http://dx.doi.org/10.1007/s10994-023-06391-0)**Paul Viallard, Pascal Germain, Amaury Habrard et al.*2 citations*
148. **[Active learning algorithm through the lens of rejection arguments ↗](https://doi.org/10.1007/s10994-023-06494-8)**Christophe Denis, Mohamed Hebiri, Boris Edgar Ndjia Njike et al.*2 citations*
149. **[Classification Algorithms ↗](http://dx.doi.org/10.1201/9781003290100-6)**T V Geetha, Selvaraju Sendhilkumar*2 citations*
150. **[Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning ↗](https://doi.org/10.1007/s10994-023-06315-y)**Clément Lejeune, Josiane Mothe, Adil Soubki et al.*2 citations*
151. **[Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events ↗](https://doi.org/10.1007/s10994-023-06424-8)**Lucas P. Damasceno, Egzona Rexhepi, Allison Shafer et al.*2 citations*
152. **[Introduction ↗](https://doi.org/10.1201/9781003290100-1)**T V Geetha, Selvaraju Sendhilkumar*2 citations*
153. **[Lagrangian objective function leads to improved unforeseen attack generalization ↗](https://doi.org/10.1007/s10994-023-06348-3)**Mohammad Azizmalayeri, Mohammad Hossein Rohban*2 citations*
154. **[Local2Global: a distributed approach for scaling representation learning on graphs ↗](https://doi.org/10.1007/s10994-022-06285-7)**Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong et al.*2 citations*
155. **[Markov chain importance sampling for minibatches ↗](https://doi.org/10.1007/s10994-023-06489-5)**Cheng–Der Fuh, Chuan‐Ju Wang, Chen-Hung Pai*2 citations*
156. **[Modeling PU learning using probabilistic logic programming ↗](https://doi.org/10.1007/s10994-023-06461-3)**Victor Verreet, Luc De Raedt, Jessa Bekker*2 citations*
157. **[Neural network structure simplification by assessing evolution in node weight magnitude ↗](https://doi.org/10.1007/s10994-023-06438-2)**Ralf Riedel, Aviv Segev*2 citations*
158. **[Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity ↗](https://doi.org/10.1007/s10994-023-06447-1)**Evangelos Michelioudakis, Alexander Artikis, Γεώργιος Παλιούρας*2 citations*
159. **[Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment ↗](http://dx.doi.org/10.1007/s10994-023-06317-w)**Théo Verhelst, Denis Mercier, Jeevan Shrestha et al.*2 citations*
160. **[Sanitized clustering against confounding bias ↗](https://doi.org/10.1007/s10994-023-06451-5)**Yinghua Yao, Yuangang Pan, Jing Li et al.*2 citations*
161. **[Scalable variable selection for two-view learning tasks with projection operators ↗](http://dx.doi.org/10.1007/s10994-023-06433-7)**Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le et al.*2 citations*
162. **[The role of mutual information in variational classifiers ↗](https://doi.org/10.1007/s10994-023-06337-6)**Matías Vera, Leonardo J. Rey Vega, Pablo Piantanida*2 citations*
163. **[Weighted neural tangent kernel: a generalized and improved network-induced kernel ↗](https://doi.org/10.1007/s10994-023-06356-3)**Lei Tan, Shutong Wu, Wenxing Zhou et al.*2 citations*
164. **[An accelerated proximal algorithm for regularized nonconvex and nonsmooth bi-level optimization ↗](https://doi.org/10.1007/s10994-023-06329-6)**Ziyi Chen, Bhavya Kailkhura, Yi Zhou*1 citation*
165. **[Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning ↗](https://doi.org/10.1007/s10994-023-06368-z)**Lingwei Zhu, Takamitsu Matsubara*1 citation*
166. **[Deep doubly robust outcome weighted learning ↗](https://doi.org/10.1007/s10994-023-06484-w)**Xiaotong Jiang, Xin Zhou, Michael R. Kosorok*1 citation*
167. **[Dense subgraphs induced by edge labels ↗](https://doi.org/10.1007/s10994-023-06377-y)**Iiro Kumpulainen, Nikolaj Tatti*1 citation*
168. **[Diverse and consistent multi-view networks for semi-supervised regression ↗](https://doi.org/10.1007/s10994-023-06305-0)**Cuong Nguyen, Arun Raja, Le Zhang et al.*1 citation*
169. **[Heuristic search of optimal machine teaching curricula ↗](http://dx.doi.org/10.1007/s10994-023-06347-4)**Manuel García Piqueras, José Hernández‐Orallo*1 citation*
170. **[Machine Learning Applications ↗](http://dx.doi.org/10.1201/9781003290100-12)**T V Geetha, Selvaraju Sendhilkumar*1 citation*
171. **[Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains ↗](https://doi.org/10.1007/s10994-023-06350-9)**Hai Nguyen, Tetsuya Sakurai*1 citation*
172. **[PAC-learning with approximate predictors ↗](https://doi.org/10.1007/s10994-023-06301-4)**Andrew James Turner, Ata Kabán*1 citation*
173. **[Scale-preserving automatic concept extraction (SPACE) ↗](https://doi.org/10.1007/s10994-023-06373-2)**Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander et al.*1 citation*
174. **[Towards accurate knowledge transfer via target-awareness representation disentanglement ↗](https://doi.org/10.1007/s10994-023-06381-2)**Xingjian Li, Xingjian Li, Di Hu et al.*1 citation*
175. **[A moment-matching metric for latent variable generative models ↗](https://doi.org/10.1007/s10994-023-06340-x)**Cédric Beaulac
176. **[Answers to exercises ↗](https://doi.org/10.1016/b978-0-32-389859-1.00015-5)**Marco Gori, Alessandro Betti, Stefano Melacci
177. **[Biquality learning: a framework to design algorithms dealing with closed-set distribution shifts ↗](https://doi.org/10.1007/s10994-023-06372-3)**Pierre Nodet, Vincent Lemaire, Alexis Bondu et al.
178. **[Calculus of variations ↗](https://doi.org/10.1016/b978-0-32-389859-1.00018-0)**
179. **[Constrained optimization ↗](https://doi.org/10.1016/b978-0-32-389859-1.00016-7)**
180. **[Deep architectures ↗](https://doi.org/10.1016/b978-0-32-389859-1.00012-x)**Marco Gori, Alessandro Betti, Stefano Melacci
181. **[Domain Based Machine Learning Applications ↗](http://dx.doi.org/10.1201/9781003290100-13)**T V Geetha, Selvaraju Sendhilkumar
182. **[Epilogue ↗](https://doi.org/10.1016/b978-0-32-389859-1.00014-3)**Marco Gori, Alessandro Betti, Stefano Melacci
183. **[Ethical Aspects of Machine Learning ↗](http://dx.doi.org/10.1201/9781003290100-14)**T V Geetha, Selvaraju Sendhilkumar
184. **[Foundations and Categories of Machine Learning Techniques ↗](http://dx.doi.org/10.1201/9781003290100-4)**T V Geetha, Selvaraju Sendhilkumar
185. **[Introduction to Deep Learning and Convolutional Neural Networks ↗](http://dx.doi.org/10.1201/9781003290100-15)**T V Geetha, Selvaraju Sendhilkumar
186. **[Kernel machines ↗](https://doi.org/10.1016/b978-0-32-389859-1.00011-8)**Marco Gori, Alessandro Betti, Stefano Melacci
187. **[Learning to increase the power of conditional randomization tests ↗](http://dx.doi.org/10.1007/s10994-023-06302-3)**Shalev Shaer, Yaniv Romano
188. **[Linear threshold machines ↗](https://doi.org/10.1016/b978-0-32-389859-1.00010-6)**Marco Gori, Alessandro Betti, Stefano Melacci
189. **[Machine Learning: Tools and Software ↗](http://dx.doi.org/10.1201/9781003290100-5)**T V Geetha, Selvaraju Sendhilkumar
190. **[Mathematical Foundations and Machine Learning ↗](http://dx.doi.org/10.1201/9781003290100-3)**T V Geetha, Selvaraju Sendhilkumar
191. **[Notes on the exercises ↗](https://doi.org/10.1016/b978-0-32-389859-1.00007-6)**
192. **[Online binary classification from similar and dissimilar data ↗](https://doi.org/10.1007/s10994-023-06434-6)**Senlin Shu, Haobo Wang, Zhuowei Wang et al.
193. **[Other Models of Deep Learning and Applications of Deep Learning ↗](http://dx.doi.org/10.1201/9781003290100-16)**T V Geetha, Selvaraju Sendhilkumar
194. **[Performance Evaluation and Ensemble Methods ↗](http://dx.doi.org/10.1201/9781003290100-8)**T V Geetha, Selvaraju Sendhilkumar
195. **[Probabilistic and Regression Based Approaches ↗](http://dx.doi.org/10.1201/9781003290100-7)**T V Geetha, Selvaraju Sendhilkumar
196. **[Reducing classifier overconfidence against adversaries through graph algorithms ↗](http://dx.doi.org/10.1007/s10994-023-06307-y)**Leonardo Vilela Teixeira, Brian A. Jalaian, Bruno Ribeiro
197. **[Regularization operators ↗](https://doi.org/10.1016/b978-0-32-389859-1.00017-9)**
198. **[Reinforcement Learning ↗](http://dx.doi.org/10.1201/9781003290100-11)**T V Geetha, Selvaraju Sendhilkumar
199. **[Scale-preserving automatic concept extraction (SPACE) ↗](https://doi.org/10.18154/rwth-2023-08788)**Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander et al.
200. **[Sequence Models ↗](http://dx.doi.org/10.1201/9781003290100-10)**T V Geetha, Selvaraju Sendhilkumar

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