Lehel Csató

发表

Online Approximations for Wind-Field Models

Dan Cornford, Manfred Opper, Lehel Csató, 2001, ICANN.

Learning inverse kinematics with structured prediction

Bernhard Schölkopf, Jan Peters, Duy Nguyen-Tuong, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Sparse Approximations to Value Functions in Reinforcement Learning

Lehel Csató, Hunor Jakab, L. Csató, 2015 .

Bayesian Network Classifier for Medical Data Analysis

Lehel Csató, Beáta Reiz, 2009, Int. J. Comput. Commun. Control.

Improving Gaussian Process Value Function Approximation in Policy Gradient Algorithms

Lehel Csató, Hunor Jakab, L. Csató, 2011, ICANN.

Data Assimilation with Sequential Gaussian Processes

Dan Cornford, Manfred Opper, Lehel Csató, 2002 .

Improving kernel locality-sensitive hashing using pre-images and bounds

Lehel Csató, Zalán Bodó, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Reinforcement learning with guided policy search using Gaussian processes

Lehel Csató, Hunor Jakab, L. Csató, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

The Use of Simple Cellular Automata in Image Processing

Lehel Csató, Zalán Bodó, 2017 .

Sparse Representation for Gaussian Process Models

Manfred Opper, Lehel Csató, M. Opper, 2000, NIPS.

Robust Automatic Mapping Algorithms in a Network Monitoring Scenario

Dan Cornford, Lehel Csató, Benjamin R. Ingram, 2010 .

A note on label propagation for semi-supervised learning

Lehel Csató, Zalán Bodó, 2015 .

Probabilistic Data Mining

Lehel Csató, L. Csató, 2008 .

Tractable inference for probabilistic data models

Ole Winther, Manfred Opper, Lehel Csató, 2003, Complex..

Active Learning with Clustering

Lehel Csató, Zalán Bodó, Zsolt Minier, 2011, Active Learning and Experimental Design @ AISTATS.

Linear spectral hashing

Lehel Csató, Zalán Bodó, L. Csató, 2014, ESANN.

Augmented hashing for semi-supervised scenarios

Lehel Csató, Zalán Bodó, 2014, ESANN.

Projected sequential Gaussian processes: A C++ tool for interpolation of large datasets with heterogeneous noise

Dan Cornford, Lehel Csató, Ben Ingram, 2011, Comput. Geosci..

Simulation-Extrapolation Gaussian Processes for Input Noise Modeling

Lehel Csató, Botond Attila Bócsi, Hunor Jakab, 2014, 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

Learning tracking control with forward models

Jan Peters, Lehel Csató, Philipp Hennig, 2012, 2012 IEEE International Conference on Robotics and Automation.

Kernel PCA based clustering for inducing features in text categorization

Lehel Csató, Zsolt Minier, 2007, ESANN.

Finding Patterns in Visualizations of Programs

Bipin Indurkhya, Lehel Csató, Razvan V. Florian, 2017, PPIG.

Alignment-based transfer learning for robot models

Jan Peters, Lehel Csató, Botond Attila Bócsi, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Dirichlet process-based component detection in state-space models

Lehel Csató, Botond Attila Bócsi, L. Csató, 2009, ESANN.

Manifold-based non-parametric learning of action-value functions

Lehel Csató, Hunor Jakab, L. Csató, 2012, ESANN.

Prüfer Number Encoding for Genetic Bayesian Network Structure Learning Algorithm

Dan Dumitrescu, Lehel Csató, Beata Reiz, 2008, 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

TAP Gibbs Free Energy, Belief Propagation and Sparsity

Ole Winther, Manfred Opper, Lehel Csató, 2001, NIPS.

Efficient Approaches to Gaussian Process Classification

Ole Winther, Manfred Opper, Ernest Fokoué, 1999, NIPS.

Hessian Corrected Input Noise Models

Lehel Csató, Botond Attila Bócsi, L. Csató, 2013, ICANN.

Wikipedia-Based Kernels for Text Categorization

Lehel Csató, Zalán Bodó, Zsolt Minier, 2007, Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007).

Hierarchical and Reweighting Cluster Kernels for Semi-Supervised Learning

Lehel Csató, Zalán Bodó, 2010, Int. J. Comput. Commun. Control.

Approximate inference for robust Gaussian process regression

Malte Kuss, Lehel Csató, Carl Edward Rasmussen, 2005 .

Novel Feature Selection and Kernel-Based Value Approximation Method for Reinforcement Learning

Lehel Csató, Hunor Jakab, 2013, ICANN.

Functional modelling with Gaussian Processes

Lehel Csató, L. Csató, 2008 .

Single View Distortion Correction using Semantic Guidance

Lehel Csató, Szabolcs Pável, Szabolcs-Botond Lőrincz, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Sparse On-Line Gaussian Processes

Lehel Csató, Manfred Opper, M. Opper, 2002, Neural Computation.

A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Dan Cornford, Lehel Csató, Benjamin R. Ingram, 2008 .

Distortion Estimation Through Explicit Modeling of the Refractive Surface pdf

Lehel Csató, Szabolcs Pável, Csanád Sándor, 2019, ICANN.

Indirect robot model learning for tracking control

Jan Peters, Lehel Csató, Botond Attila Bócsi, 2014, Adv. Robotics.

Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

Lehel Csató, Isah Charles Saidu, L. Csató, 2021, J. Imaging.

Dicussion on the meeting on ‘Statistical approaches to inverse problems’

Simon J. Godsill, Dan Cornford, Eric Moulines, 2004 .

Tree-Like Bayesian Network Classifiers for Surgery Survival Chance Prediction

Lehel Csató, Beáta Reiz, 2008 .

Using Gaussian Processes for Variance Reduction in Policy Gradient Algorithms

Lehel Csató, Hunor Jakab, L. Csató, 2010 .

Max – Planck – Institut f ür biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No . 136 Approximate Inference for Robust Gaussian Process Regression

Carl E. Rasmussen, Malte Kuss, Lehel Csató, 2005 .

Sequential, Bayesian Geostatistics: A Principled Method for Large Data Sets

Dan Cornford, Manfred Opper, Lehel Csató, 2005 .