Sándor Szedmák

65 papers A* 2A 4B 7C 3Journal 24Unranked 25

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	Efficient Microservice Monitoring Via Kernel Transformation and FFT Forecasting. CLOUD Marianna Ojanen, Maryam Sabzevari, Sándor Szedmák
2025	J	`jnl`	Scaling up drug combination surface prediction. Briefings Bioinform. Riikka Huusari, Tianduanyi Wang, Sándor Szedmák, Diogo Dias, Tero Aittokallio, Juho Rousu
2024	J	`jnl`	Protein function prediction through multi-view multi-label latent tensor reconstruction. BMC Bioinform. Robert Ebo Armah-Sekum, Sándor Szedmák, Juho Rousu
2024	J	`jnl`	Scalable variable selection for two-view learning tasks with projection operators. Mach. Learn. Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu
2023	—	`conf`	Personalized Learning Systems for Computer Science Students: Analyzing and Predicting Learning Behaviors Using Programming Error Data. UMAP (Adjunct Publication) Mubina Kamberovic, Senka Krivic, Amra Delic, Sándor Szedmák, Vedran Ljubovic
2023	J	`jnl`	Scalable variable selection for two-view learning tasks with projection operators. CoRR Sándor Szedmák, Riikka Huusari, Tat Hong Duong Le, Juho Rousu
2022	J	`jnl`	Strain design optimization using reinforcement learning. PLoS Comput. Biol. Maryam Sabzevari, Sándor Szedmák, Merja Penttilä, Paula Jouhten, Juho Rousu
2021	J	`jnl`	Modeling drug combination effects via latent tensor reconstruction. Bioinform. Tianduanyi Wang, Sándor Szedmák, Haishan Wang, Tero Aittokallio, Tapio Pahikkala, Anna Cichonska, Juho Rousu
2020	J	`jnl`	A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction. CoRR Sándor Szedmák, Anna Cichonska, Heli Julkunen, Tapio Pahikkala, Juho Rousu
2020	J	`jnl`	Using Machine Learning for Decreasing State Uncertainty in Planning. J. Artif. Intell. Res. Senka Krivic, Michael Cashmore, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater
2018	J	`jnl`	Integrating multi-purpose natural language understanding, robot's memory, and symbolic planning for task execution in humanoid robots. Robotics Auton. Syst. Mirko Wächter, Ekaterina Ovchinnikova, Valerij Wittenbeck, Peter Kaiser, Sándor Szedmák, Wail Mustafa, Dirk Kraft, Norbert Krüger, Justus H. Piater, Tamim Asfour
2018	J	`jnl`	Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinform. Anna Cichonska, Tapio Pahikkala, Sándor Szedmák, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu
2018	J	`jnl`	Liquid-chromatography retention order prediction for metabolite identification. Bioinform. Eric Bach, Sándor Szedmák, Céline Brouard, Sebastian Böcker, Juho Rousu
2017	A*	`conf`	Decreasing Uncertainty in Planning with State Prediction. IJCAI Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sándor Szedmák, Justus H. Piater
2017	—	`conf`	Initial State Prediction in Planning. AAAI Workshops Senka Krivic, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sándor Szedmák, Justus H. Piater
2017	J	`jnl`	Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images. Neurocomputing Kitsuchart Pasupa, Sándor Szedmák
2016	J	`jnl`	Hierarchical Haptic Manipulation for Complex Skill Learning. CoRR Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater
2016	—	`conf`	Kronecker Decomposition for Image Classification. CLEF Sabrina Fontanella, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák
2016	J	`jnl`	Learning undirected graphical models using persistent sequential Monte Carlo. Mach. Learn. Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2016	A	`conf`	Robotic playing for hierarchical complex skill learning. IROS Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater
2016	B	`conf`	Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning. DS Huibin Shen, Sándor Szedmák, Céline Brouard, Juho Rousu
2015	—	`conf`	Can Computer Vision Problems Benefit from Structured Hierarchical Classification? CAIP (2) Thomas Hoyoux, Antonio Jose Rodríguez-Sánchez, Justus H. Piater, Sándor Szedmák
2015	J	`jnl`	Diversity priors for learning early visual features. Frontiers Comput. Neurosci. Hanchen Xiong, Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater
2015	—	`conf`	IIS at ImageCLEF 2015: Multi-label Classification Task. CLEF (Working Notes) Antonio Jose Rodríguez-Sánchez, Sabrina Fontanella, Justus H. Piater, Sándor Szedmák
2015	B	`conf`	Learning missing edges via kernels in partially-known graphs. ESANN Senka Krivic, Sándor Szedmák, Hanchen Xiong, Justus H. Piater
2015	—	`conf`	Learning to Predict Where People Look with Tensor-Based Multi-view Learning. ICONIP (1) Kitsuchart Pasupa, Sándor Szedmák
2015	—	`conf`	Multi-label Object Categorization Using Histograms of Global Relations. 3DV Wail Mustafa, Hanchen Xiong, Dirk Kraft, Sándor Szedmák, Justus H. Piater, Norbert Krüger
2015	—	`conf`	Reactive, task-specific object manipulation by metric reinforcement learning. ICAR Simon Hangl, Emre Ugur, Sándor Szedmák, Justus H. Piater, Ales Ude
2015	A	`conf`	SCurV: A 3D descriptor for object classification. IROS Antonio Jose Rodríguez-Sánchez, Sándor Szedmák, Justus H. Piater
2015	J	`jnl`	Scalable, accurate image annotation with joint SVMs and output kernels. Neurocomputing Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2015	A	`conf`	Using structural bootstrapping for object substitution in robotic executions of human-like manipulation tasks. IROS Alejandro Agostini, Mohamad Javad Aein, Sándor Szedmák, Eren Erdal Aksoy, Justus H. Piater, Florentin Wörgötter
2014	—	`conf`	Bootstrapping paired-object affordance learning with learned single-affordance features. ICDL-EPIROB Emre Ugur, Sándor Szedmák, Justus H. Piater
2014	—	`conf`	Complex affordance learning based on basic affordances. SIU Emre Ugur, Sándor Szedmák, Justus H. Piater
2014	B	`conf`	Joint SVM for Accurate and Fast Image Tagging. ESANN Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2014	A	`conf`	Knowledge propagation and relation learning for predicting action effects. IROS Sándor Szedmák, Emre Ugur, Justus H. Piater
2014	C	`conf`	Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo. ACML Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2014	C	`conf`	Towards Sparsity and Selectivity: Bayesian Learning of Restricted Boltzmann Machine for Early Visual Features. ICANN Hanchen Xiong, Sándor Szedmák, Antonio Jose Rodríguez-Sánchez, Justus H. Piater
2013	—	`conf`	3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields. GCPR Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2013	—	`conf`	A Study of Point Cloud Registration with Probability Product Kernel Functions. 3DV Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2013	—	`conf`	Efficient, General Point Cloud Registration with Kernel Feature Maps. CRV Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2013	—	`conf`	Homogeneity analysis for object-action relation reasoning in kitchen scenarios. MLIS@IJCAI Hanchen Xiong, Sándor Szedmák, Justus H. Piater
2012	J	`jnl`	Kernel-Mapping Recommender system algorithms. Inf. Sci. Mustansar Ali Ghazanfar, Adam Prügel-Bennett, Sándor Szedmák
2011	J	`jnl`	Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. J. Mach. Learn. Res. Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan
2011	B	`conf`	Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. ICTAI Mustansar Ali Ghazanfar, Sándor Szedmák, Adam Prügel-Bennett
2010	—	`conf`	Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. WAPA Sándor Szedmák, Yizhao Ni, Steve R. Gunn
2010	—	`conf`	Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function. BIOINFORMATICS Katja Astikainen, Esa Pitkänen, Juho Rousu, Liisa Holm, Sándor Szedmák
2010	—	`conf`	Structured Output Prediction of Novel Enzyme Function with Reaction Kernels. BIOSTEC (Selected Papers) Katja Astikainen, Liisa Holm, Esa Pitkänen, Sándor Szedmák, Juho Rousu
2010	J	`jnl`	The application of structured learning in natural language processing. Mach. Transl. Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan
2009	—	`conf`	Handling phrase reorderings for machine translation. ACL/IJCNLP (2) Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan
2009	—	`conf`	Large scale maximum margin regression based, structural learning approach to phrase translations. SMART@EAMT Sándor Szedmák, Esther Galbrun, Craig Saunders, Yizhao Ni
2009	—	`conf`	Large-margin structural prediction via linear programming. SMART@EAMT Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
2009	—	`conf`	Learning to rank images from eye movements. ICCV Workshops Kitsuchart Pasupa, Craig Saunders, Sándor Szedmák, Arto Klami, Samuel Kaski, Steve R. Gunn
2007	B	`conf`	A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. ESANN Sándor Szedmák, Tijl De Bie, David R. Hardoon
2007	—	`conf`	Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
2007	J	`jnl`	Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing Sándor Szedmák, John Shawe-Taylor
2006	C	`conf`	A Correlation Approach for Automatic Image Annotation. ADMA David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2006	J	`jnl`	Kernel-Based Learning of Hierarchical Multilabel Classification Models. J. Mach. Learn. Res. Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2006	B	`conf`	Synthesis of maximum margin and multiview learning using unlabeled data. ESANN Sándor Szedmák, John Shawe-Taylor
2005	A*	`conf`	Learning hierarchical multi-category text classification models. ICML Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2005	—	`conf`	The 2005 PASCAL Visual Object Classes Challenge. MLCW Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang
2005	—	`conf`	Two view learning: SVM-2K, Theory and Practice. NIPS Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák
2004	J	`jnl`	Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. David R. Hardoon, Sándor Szedmák, John Shawe-Taylor
2004	J	`jnl`	Pareto-optimal patterns in logical analysis of data. Discret. Appl. Math. Peter L. Hammer, Alexander Kogan, Bruno Simeone, Sándor Szedmák
2004	J	`jnl`	Saturated systems of homogeneous boxes and the logical analysis of numerical data. Discret. Appl. Math. Peter L. Hammer, Yanpei Liu, Bruno Simeone, Sándor Szedmák
2004	—	`conf`	Support Vector Machine to Synthesise Kernels. Deterministic and Statistical Methods in Machine Learning Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar