Search Results for author: Jonas Rothfuss

Found 12 papers, 8 papers with code

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Meta-Learning Reliable Priors in the Function Space

no code implementations NeurIPS 2021 Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause

Meta-Learning promises to enable more data-efficient inference by harnessing previous experience from related learning tasks.

Decision Making Meta-Learning

DiBS: Differentiable Bayesian Structure Learning

1 code implementation NeurIPS 2021 Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause

In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.

Causal Discovery Variational Inference

Robustness to Pruning Predicts Generalization in Deep Neural Networks

no code implementations10 Mar 2021 Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal

Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural networks.

Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory

no code implementations1 Jan 2021 Jonas Rothfuss, Martin Josifoski, Andreas Krause

Bayesian deep learning is a promising approach towards improved uncertainty quantification and sample efficiency.

Meta-Learning Variational Inference

Noise Regularization for Conditional Density Estimation

1 code implementation21 Jul 2019 Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause

To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.

Density Estimation

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

1 code implementation3 Mar 2019 Jonas Rothfuss, Fabio Ferreira, Simon Walther, Maxim Ulrich

Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$.

Density Estimation

Model-Based Reinforcement Learning via Meta-Policy Optimization

no code implementations14 Sep 2018 Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.

Model-based Reinforcement Learning

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets

2 code implementations2 Jul 2018 Fabio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour

We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

1 code implementation12 Jan 2018 Jonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour

We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.

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