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ORCIDPKDD / ECML 2022: Grenoble, France - Part V
- Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V. Lecture Notes in Computer Science 13717, Springer 2023, ISBN 978-3-031-26418-4
Supervised Learning
- Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn:
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. 3-19 - David Rügamer
, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. 20-35 - Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani, Martin Senz:
Ordinal Quantification Through Regularization. 36-52 - Maciej Piernik, Dariusz Brzezinski, Pawel Zawadzki:
Random Similarity Forests. 53-69 - Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic:
Spectral Ranking with Covariates. 70-86 - Lincen Yang, Matthijs van Leeuwen:
Truly Unordered Probabilistic Rule Sets for Multi-class Classification. 87-103
Probabilistic Inference
- Shuyu Dong, Michèle Sebag:
From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm. 107-122 - Shu Yu Tew, Daniel F. Schmidt, Enes Makalic:
Sparse Horseshoe Estimation via Expectation-Maximisation. 123-139 - Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe:
Structure-Preserving Gaussian Process Dynamics. 140-156 - Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama:
Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation. 157-173 - Shirin Goshtasbpour, Fernando Pérez-Cruz:
Optimization of Annealed Importance Sampling Hyperparameters. 174-190 - Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla:
Bayesian Nonparametrics for Sparse Dynamic Networks. 191-206 - Thibaud Rahier, Sylvain Marié, Florence Forbes:
A Pre-screening Approach for Faster Bayesian Network Structure Learning. 207-222 - Sagar Malhotra, Luciano Serafini:
On Projectivity in Markov Logic Networks. 223-238 - Mina Rafla, Nicolas Voisine, Bruno Crémilleux, Marc Boullé:
A Non-parametric Bayesian Approach for Uplift Discretization and Feature Selection. 239-254 - Dario Simionato, Fabio Vandin:
Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages. 255-271
Optimal Transport
- Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Learning Optimal Transport Between Two Empirical Distributions with Normalizing Flows. 275-290 - Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada:
Feature-Robust Optimal Transport for High-Dimensional Data. 291-307
Optimization
- Douglas J. Leith, George Iosifidis:
Penalized FTRL with Time-Varying Constraints. 311-326 - Constantin Octavian Puiu:
Rethinking Exponential Averaging of the Fisher. 327-343 - Bodo Rosenhahn:
Mixed Integer Linear Programming for Optimizing a Hopfield Network. 344-360 - Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, Lars Schmidt-Thieme:
Learning to Control Local Search for Combinatorial Optimization. 361-376 - Zeren Huang, Wenhao Chen, Weinan Zhang, Chuhan Shi, Furui Liu, Hui-Ling Zhen, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang:
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-Based Policy Learning. 377-392 - Gaël Aglin, Siegfried Nijssen, Pierre Schaus:
Learning Optimal Decision Trees Under Memory Constraints. 393-409 - Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
SaDe: Learning Models that Provably Satisfy Domain Constraints. 410-425 - Sahil Manchanda, Sofia Michel, Darko Drakulic, Jean-Marc Andreoli:
On the Generalization of Neural Combinatorial Optimization Heuristics. 426-442 - Harold Silvère Kiossou, Pierre Schaus, Siegfried Nijssen, Vinasétan Ratheil Houndji:
Time Constrained DL8.5 Using Limited Discrepancy Search. 443-459
Quantum, Hardware
- Kurt Stolle, Sebastian Vogel, Fons van der Sommen, Willem P. Sanberg:
Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search. 463-479 - John Osorio Ríos, Adrià Armejach, Eric Petit, Greg Henry, Marc Casas:
FASE: A Fast, Accurate and Seamless Emulator for Custom Numerical Formats. 480-497 - Xin Liu, Mingyu Yan, Shuhan Song, Zhengyang Lv, Wenming Li, Guangyu Sun, Xiaochun Ye, Dongrui Fan:
GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware. 498-514 - Christof Wendenius, Eileen Kuehn, Achim Streit:
Training Parameterized Quantum Circuits with Triplet Loss. 515-530 - Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen:
Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests. 531-546
Sustainability
- Zhao Geng, Ziqing Gao, Tsai Chihsu, Jiamin Lu:
CGPM: Poverty Mapping Framework Based on Multi-Modal Geographic Knowledge Integration and Macroscopic Social Network Mining. 549-564 - Firas Gerges, Michel C. Boufadel, Elie Bou-Zeid, Ankit Darekar, Hani Nassif, Jason T. L. Wang:
Bayesian Multi-head Convolutional Neural Networks with Bahdanau Attention for Forecasting Daily Precipitation in Climate Change Monitoring. 565-580 - Mahsa Keramati, Mohammad A. Tayebi, Zahra Zohrevand, Uwe Glässer, Juan Anzieta, Glyn Williams-Jones:
Cubism: Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer. 581-597 - Karthik S. Gurumoorthy, Abhiraj Hinge:
Go Green: A Decision-Tree Framework to Select Optimal Box-Sizes for Product Shipments. 598-613 - Alban Puech, Jesse Read:
An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learning. 614-630
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