activity
20152019
most citedDiscriminative training for Convolved Multiple-Output Gaussian processes

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG2019

Real Time Trajectory Prediction Using Deep Conditional Generative Models

Sebastian Gomez-Gonzalez, Sergey Prokudin, Bernhard Scholkopf +1

Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot syste…

cs.RO2019

Reliable Real Time Ball Tracking for Robot Table Tennis

Sebastian Gomez-Gonzalez, Yassine Nemmour, Bernhard Schölkopf +1

Robot table tennis systems require a vision system that can track the ball position with low latency and high sampling rate. Altering the ball to simplify the tracking using for in…

cs.LG2019

Bayesian Online Prediction of Change Points

Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer +2

Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their…

cs.LG2018

Adaptation and Robust Learning of Probabilistic Movement Primitives

Sebastian Gomez-Gonzalez, Gerhard Neumann, Bernhard Schölkopf +1

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of…

stat.ML20151 cited

Discriminative training for Convolved Multiple-Output Gaussian processes

Sebastián Gómez-González, Mauricio A. Álvarez, Hernán Felipe García

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class cla…