van der Smagt, Patrick

24 publications

TMLR 2025 Inherently Robust Control Through Maximum-Entropy Learning-Based Rollout Felix Bok, Atanas Mirchev, Baris Kayalibay, Ole Jonas Wenzel, Patrick van der Smagt, Justin Bayer

TMLR 2025 Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

NeurIPS 2024 Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

CoRL 2024 Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters Ahmed Agha, Baris Kayalibay, Atanas Mirchev, Patrick van der Smagt, Justin Bayer

ICMLW 2024 Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

NeurIPS 2023 Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

ICLRW 2023 Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

L4DC 2023 CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces Elie Aljalbout, Maximilian Karl, Patrick van der Smagt

L4DC 2023 Filter-Aware Model-Predictive Control Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer

CoRL 2022 PRISM: Probabilistic Real-Time Inference in Spatial World Models Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt, Daniel Cremers, Justin Bayer

ICMLW 2021 Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt

NeurIPS 2021 Latent Matters: Learning Deep State-Space Models Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt

ICLR 2021 Mind the Gap When Conditioning Amortised Inference in Sequential Latent-Variable Models Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt

ICLR 2021 Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer

ICLR 2020 Continual Learning with Bayesian Neural Networks for Non-Stationary Data Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann

ICML 2020 Learning Flat Latent Manifolds with VAEs Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick Van Der Smagt

NeurIPS 2019 Learning Hierarchical Priors in VAEs Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt

AAAI 2019 Multi-Source Neural Variational Inference Richard Kurle, Stephan Günnemann, Patrick van der Smagt

ICML 2019 Switching Linear Dynamics for Variational Bayes Filtering Philip Becker-Ehmck, Jan Peters, Patrick Van Der Smagt

AISTATS 2018 Metrics for Deep Generative Models Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt

ICLR 2017 Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt

ICCV 2015 FlowNet: Learning Optical Flow with Convolutional Networks Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox

ICLR 2014 On Fast Dropout and Its Applicability to Recurrent Networks Justin Bayer, Christian Osendorfer, Nutan Chen, Sebastian Urban, Patrick van der Smagt

ICLR 2013 Unsupervised Feature Learning for Low-Level Local Image Descriptors Christian Osendorfer, Justin Bayer, Patrick van der Smagt