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ORCIDPKDD / ECML 2023: Turin, Italy - Part IV
- Danai Koutra
, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi:
Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part IV. Lecture Notes in Computer Science 14172, Springer 2023, ISBN 978-3-031-43420-4
Natural Language Processing
- Chuanyu Jiang, Yiming Qian, Lijun Chen, Yang Gu, Xia Xie:
Unsupervised Deep Cross-Language Entity Alignment. 3-19 - Sizhe Zhou, Suyu Ge, Jiaming Shen, Jiawei Han:
Corpus-Based Relation Extraction by Identifying and Refining Relation Patterns. 20-38 - Weichen Li, Rati Devidze, Sophie Fellenz:
Learning to Play Text-Based Adventure Games with Maximum Entropy Reinforcement Learning. 39-54 - Haotian Chen, Han Zhang, Houjing Guo, Shuchang Yi, Bingsheng Chen, Xiangdong Zhou:
SALAS: Supervised Aspect Learning Improves Abstractive Multi-document Summarization Through Aspect Information Loss. 55-70 - Neeraj Kumar, Ankur Narang, Brejesh Lall:
KL Regularized Normalization Framework for Low Resource Tasks. 71-89 - Victor Agostinelli, Lizhong Chen:
Improving Autoregressive NLP Tasks via Modular Linearized Attention. 90-106 - Tianyun Liu, Xinghua Zhang, Zhenyu Zhang, Yubin Wang, Quangang Li, Shuai Zhang, Tingwen Liu:
Enhancing Table Retrieval with Dual Graph Representations. 107-123 - Vivek Srivastava, Savita Bhat, Niranjan Pedanekar:
A Few Good Sentences: Content Selection for Abstractive Text Summarization. 124-141 - Jiayao Chen, Rui Wang, Jueying He, Mark Junjie Li:
Encouraging Sparsity in Neural Topic Modeling with Non-Mean-Field Inference. 142-158
Neuro/Symbolic Learning
- Amanda Bertschinger, Q. Tyrell Davis, James P. Bagrow, Josh C. Bongard:
The Metric is the Message: Benchmarking Challenges for Neural Symbolic Regression. 161-177 - Nan Jiang, Yexiang Xue:
Symbolic Regression via Control Variable Genetic Programming. 178-195 - N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo:
Neural Class Expression Synthesis in ALCHIQ(D). 196-212 - Rishi Hazra, Luc De Raedt:
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. 213-229 - Monika Jain, Kuldeep Singh, Raghava Mutharaju:
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction. 230-247
Optimization
- Ying Sun, Hongwei Yong, Lei Zhang:
NKFAC: A Fast and Stable KFAC Optimizer for Deep Neural Networks. 251-267 - Mehdi Seyfi, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang:
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks. 268-283 - Khadija Musayeva, Mickaël Binois:
Improved Multi-label Propagation for Small Data with Multi-objective Optimization. 284-300 - Chen Fan, Christos Thrampoulidis, Mark Schmidt:
Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition. 301-315 - Etika Agarwal, Karthik S. Gurumoorthy, Ankit Ajit Jain, Shantala Manchenahally:
A Scalable Solution for the Extended Multi-channel Facility Location Problem. 316-332 - Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang:
Online State Exploration: Competitive Worst Case and Learning-Augmented Algorithms. 333-348 - Alexandre Hippert-Ferrer, Florent Bouchard, Ammar Mian, Titouan Vayer, Arnaud Breloy:
Learning Graphical Factor Models with Riemannian Optimization. 349-366
Recommender Systems
- Boyu Li, Ting Guo, Xingquan Zhu, Yang Wang, Fang Chen:
ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation. 369-386 - Qian Zhao, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou:
Long-Tail Augmented Graph Contrastive Learning for Recommendation. 387-403 - Pengyu Zhao, Shoujin Wang, Wenpeng Lu, Xueping Peng, Weiyu Zhang, Chaoqun Zheng, Yonggang Huang:
News Recommendation via Jointly Modeling Event Matching and Style Matching. 404-419 - Harshit Mishra, Sucheta Soundarajan:
BalancedQR: A Framework for Balanced Query Recommendation. 420-435
Reinforcement Learning
- Jie Dai, Xuguang Chen:
On the Distributional Convergence of Temporal Difference Learning. 439-454 - Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist:
Offline Reinforcement Learning with On-Policy Q-Function Regularization. 455-471 - Arpan Dasgupta, Pawan Kumar:
Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In During Sparse Matrix Decomposition. 472-488 - Jan Wöhlke, Felix Schmitt, Herke van Hoof:
Learning Hierarchical Planning-Based Policies from Offline Data. 489-505 - Luca Sabbioni, Francesco Corda, Marcello Restelli:
Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes. 506-523 - Nam Phuong Tran, Long Tran-Thanh:
Invariant Lipschitz Bandits: A Side Observation Approach. 524-539 - Linghui Meng, Xuantang Xiong, Yifan Zang, Xi Zhang, Guoqi Li, Dengpeng Xing, Bo Xu:
Filtered Observations for Model-Based Multi-agent Reinforcement Learning. 540-555 - Zhaohui Jiang, Paul Weng:
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning. 556-572 - Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi:
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning. 573-589
Representation Learning
- David Friede, Christian Reimers, Heiner Stuckenschmidt, Mathias Niepert:
Learning Disentangled Discrete Representations. 593-609 - Quentin Delfosse, Wolfgang Stammer, Thomas Rothenbacher, Dwarak Vittal, Kristian Kersting:
Boosting Object Representation Learning via Motion and Object Continuity. 610-628 - Alfredo Reichlin, Giovanni Luca Marchetti, Hang Yin, Anastasiia Varava, Danica Kragic:
Learning Geometric Representations of Objects via Interaction. 629-644 - Manuele Bicego, Ferdinando Cicalese:
On the Good Behaviour of Extremely Randomized Trees in Random Forest-Distance Computation. 645-660 - Filip Szatkowski, Karol J. Piczak, Przemyslaw Spurek, Jacek Tabor, Tomasz Trzcinski:
Hypernetworks Build Implicit Neural Representations of Sounds. 661-676 - Priyanka Chudasama, Tushar Kadam, Rajat Patel, Aakarsh Malhotra, Manoj Magam:
Contrastive Representation Through Angle and Distance Based Loss for Partial Label Learning. 677-692 - Luis Armando Pérez Rey, Giovanni Luca Marchetti, Danica Kragic, Dmitri Jarnikov, Mike Holenderski:
Equivariant Representation Learning in the Presence of Stabilizers. 693-708 - Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa, Yuhui Zhang, Takafumi Kanamori:
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. 709-727 - Pranav Poduval, Gaurav Oberoi, Sangam Verma, Ayush Agarwal, Karamjit Singh, Siddhartha Asthana:
BipNRL: Mutual Information Maximization on Bipartite Graphs for Node Representation Learning. 728-743
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