Support Vector Machine to Synthesise Kernels. - Hongying Meng; John Shawe-Taylor; Sándor Szedmák; Jason D. R. Farquhar (Deterministic and Statistical Methods in Machine Learning, 2004)

Pareto-optimal patterns in logical analysis of data. - Peter L. Hammer; Alexander Kogan; Bruno Simeone; Sándor Szedmák (Discret. Appl. Math., 2004)

Saturated systems of homogeneous boxes and the logical analysis of numerical data. - Peter L. Hammer; Yanpei Liu; Bruno Simeone; Sándor Szedmák (Discret. Appl. Math., 2004)

Canonical Correlation Analysis: An Overview with Application to Learning Methods. - David R. Hardoon; Sándor Szedmák; John Shawe-Taylor (Neural Comput., 2004)

Learning hierarchical multi-category text classification models. - Juho Rousu; Craig Saunders; Sándor Szedmák; John Shawe-Taylor (ICML, 2005)

The 2005 PASCAL Visual Object Classes Challenge. - 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; et al. (MLCW, 2005)

Two view learning: SVM-2K, Theory and Practice. - Jason D. R. Farquhar; David R. Hardoon; Hongying Meng; John Shawe-Taylor; Sándor Szedmák (NIPS, 2005)

A Correlation Approach for Automatic Image Annotation. - David R. Hardoon; Craig Saunders; Sándor Szedmák; John Shawe-Taylor (ADMA, 2006)

Kernel-Based Learning of Hierarchical Multilabel Classification Models. - Juho Rousu; Craig Saunders; Sándor Szedmák; John Shawe-Taylor (J. Mach. Learn. Res., 2006)

A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. - Sándor Szedmák; Tijl De Bie; David R. Hardoon (ESANN, 2007)

Kernel Regression Based Machine Translation. - Zhuoran Wang; John Shawe-Taylor; Sándor Szedmák (HLT-NAACL (Short Papers), 2007)

Synthesis of maximum margin and multiview learning using unlabeled data. - Sándor Szedmák; John Shawe-Taylor (Neurocomputing, 2007)

Handling phrase reorderings for machine translation. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (ACL/IJCNLP (2), 2009)

Large scale maximum margin regression based, structural learning approach to phrase translations. - Sándor Szedmák; Esther Galbrun; Craig Saunders; Yizhao Ni (SMART@EAMT, 2009)

Large-margin structural prediction via linear programming. - Zhuoran Wang; John Shawe-Taylor; Sándor Szedmák (SMART@EAMT, 2009)

Learning to rank images from eye movements. - Kitsuchart Pasupa; Craig Saunders; Sándor Szedmák; Arto Klami; Samuel Kaski; Steve R. Gunn (ICCV Workshops, 2009)

Structured Output Prediction of Novel Enzyme Function with Reaction Kernels. - Katja Astikainen; Liisa Holm; Esa Pitkänen; Sándor Szedmák; Juho Rousu (BIOSTEC (Selected Papers), 2010)

Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function. - Katja Astikainen; Esa Pitkänen; Juho Rousu; Liisa Holm; Sándor Szedmák (BIOINFORMATICS, 2010)

Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. - Sándor Szedmák; Yizhao Ni; Steve R. Gunn (WAPA, 2010)

The application of structured learning in natural language processing. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (Mach. Transl., 2010)

Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. - Mustansar Ali Ghazanfar; Sándor Szedmák; Adam Prügel-Bennett (ICTAI, 2011)

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. - Yizhao Ni; Craig Saunders; Sándor Szedmák; Mahesan Niranjan (J. Mach. Learn. Res., 2011)

Kernel-Mapping Recommender system algorithms. - Mustansar Ali Ghazanfar; Adam Prügel-Bennett; Sándor Szedmák (Inf. Sci., 2012)

A Study of Point Cloud Registration with Probability Product Kernel Functions. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (3DV, 2013)

Efficient, General Point Cloud Registration with Kernel Feature Maps. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (CRV, 2013)

3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (GCPR, 2013)

Homogeneity analysis for object-action relation reasoning in kitchen scenarios. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (MLIS@IJCAI, 2013)

Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (ACML, 2014)

Joint SVM for Accurate and Fast Image Tagging. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (ESANN, 2014)

Towards Sparsity and Selectivity: Bayesian Learning of Restricted Boltzmann Machine for Early Visual Features. - Hanchen Xiong; Sándor Szedmák; Antonio Jose Rodríguez-Sánchez; Justus H. Piater (ICANN, 2014)

Bootstrapping paired-object affordance learning with learned single-affordance features. - Emre Ugur; Sándor Szedmák; Justus H. Piater (ICDL-EPIROB, 2014)

Knowledge propagation and relation learning for predicting action effects. - Sándor Szedmák; Emre Ugur; Justus H. Piater (IROS, 2014)

Complex affordance learning based on basic affordances. - Emre Ugur; Sándor Szedmák; Justus H. Piater (SIU, 2014)

Multi-label Object Categorization Using Histograms of Global Relations. - Wail Mustafa; Hanchen Xiong; Dirk Kraft; Sándor Szedmák; Justus H. Piater; Norbert Krüger (3DV, 2015)

Can Computer Vision Problems Benefit from Structured Hierarchical Classification? - Thomas Hoyoux; Antonio Jose Rodríguez-Sánchez; Justus H. Piater; Sándor Szedmák (CAIP (2), 2015)

IIS at ImageCLEF 2015: Multi-label Classification Task. - Antonio Jose Rodríguez-Sánchez; Sabrina Fontanella; Justus H. Piater; Sándor Szedmák (CLEF (Working Notes), 2015)

Learning missing edges via kernels in partially-known graphs. - Senka Krivic; Sándor Szedmák; Hanchen Xiong; Justus H. Piater (ESANN, 2015)

Reactive, task-specific object manipulation by metric reinforcement learning. - Simon Hangl; Emre Ugur; Sándor Szedmák; Justus H. Piater; Ales Ude (ICAR, 2015)

Learning to Predict Where People Look with Tensor-Based Multi-view Learning. - Kitsuchart Pasupa; Sándor Szedmák (ICONIP (1), 2015)

Using structural bootstrapping for object substitution in robotic executions of human-like manipulation tasks. - Alejandro Agostini; Mohamad Javad Aein; Sándor Szedmák; Eren Erdal Aksoy; Justus H. Piater; Florentin Wörgötter (IROS, 2015)

SCurV: A 3D descriptor for object classification. - Antonio Jose Rodríguez-Sánchez; Sándor Szedmák; Justus H. Piater (IROS, 2015)

Diversity priors for learning early visual features. - Hanchen Xiong; Antonio Jose Rodríguez-Sánchez; Sándor Szedmák; Justus H. Piater (Frontiers Comput. Neurosci., 2015)

Scalable, accurate image annotation with joint SVMs and output kernels. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (Neurocomputing, 2015)

Kronecker Decomposition for Image Classification. - Sabrina Fontanella; Antonio Jose Rodríguez-Sánchez; Justus H. Piater; Sándor Szedmák (CLEF, 2016)

Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning. - Huibin Shen; Sándor Szedmák; Céline Brouard; Juho Rousu (DS, 2016)

Robotic playing for hierarchical complex skill learning. - Simon Hangl; Emre Ugur; Sándor Szedmák; Justus H. Piater (IROS, 2016)

Hierarchical Haptic Manipulation for Complex Skill Learning. - Simon Hangl; Emre Ugur; Sándor Szedmák; Justus H. Piater (CoRR, 2016)

Learning undirected graphical models using persistent sequential Monte Carlo. - Hanchen Xiong; Sándor Szedmák; Justus H. Piater (Mach. Learn., 2016)

Initial State Prediction in Planning. - Senka Krivic; Michael Cashmore; Bram Ridder; Daniele Magazzeni; Sándor Szedmák; Justus H. Piater (AAAI Workshops, 2017)

Decreasing Uncertainty in Planning with State Prediction. - Senka Krivic; Michael Cashmore; Daniele Magazzeni; Bram Ridder; Sándor Szedmák; Justus H. Piater (IJCAI, 2017)

Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images. - Kitsuchart Pasupa; Sándor Szedmák (Neurocomputing, 2017)

Liquid-chromatography retention order prediction for metabolite identification. - Eric Bach 002; Sándor Szedmák; Céline Brouard; Sebastian Böcker; Juho Rousu (Bioinform., 2018)

Learning with multiple pairwise kernels for drug bioactivity prediction. - Anna Cichonska; Tapio Pahikkala; Sándor Szedmák; Heli Julkunen; Antti Airola; Markus Heinonen; Tero Aittokallio; Juho Rousu (Bioinform., 2018)

Integrating multi-purpose natural language understanding, robot's memory, and symbolic planning for task execution in humanoid robots. - Mirko Wächter; Ekaterina Ovchinnikova; Valerij Wittenbeck; Peter Kaiser 001; Sándor Szedmák; Wail Mustafa; Dirk Kraft; Norbert Krüger; Justus H. Piater; Tamim Asfour (Robotics Auton. Syst., 2018)

A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction. - Sándor Szedmák; Anna Cichonska; Heli Julkunen; Tapio Pahikkala; Juho Rousu (CoRR, 2020)

Using Machine Learning for Decreasing State Uncertainty in Planning. - Senka Krivic; Michael Cashmore; Daniele Magazzeni; Sándor Szedmák; Justus H. Piater (J. Artif. Intell. Res., 2020)

Modeling drug combination effects via latent tensor reconstruction. - Tianduanyi Wang; Sándor Szedmák; Haishan Wang; Tero Aittokallio; Tapio Pahikkala; Anna Cichonska; Juho Rousu (Bioinform., 2021)

Strain design optimization using reinforcement learning. - Maryam Sabzevari; Sándor Szedmák; Merja Penttilä; Paula Jouhten; Juho Rousu (PLoS Comput. Biol., 2022)

Personalized Learning Systems for Computer Science Students: Analyzing and Predicting Learning Behaviors Using Programming Error Data. - Mubina Kamberovic; Senka Krivic; Amra Delic; Sándor Szedmák; Vedran Ljubovic (UMAP (Adjunct Publication), 2023)

Scalable variable selection for two-view learning tasks with projection operators. - Sándor Szedmák; Riikka Huusari; Tat Hong Duong Le; Juho Rousu (Mach. Learn., 2024)

Protein function prediction through multi-view multi-label latent tensor reconstruction. - Robert Ebo Armah-Sekum; Sándor Szedmák; Juho Rousu (BMC Bioinform., 2024)

Scaling up drug combination surface prediction. - Riikka Huusari; Tianduanyi Wang; Sándor Szedmák; Diogo Dias; Tero Aittokallio; Juho Rousu (Briefings Bioinform., 2025)