Jonas Rothfuss | DeepAI

AI Chat

Featured Co-authors

Yoshua Bengio
448 publications
Bernhard Schölkopf
271 publications
Pieter Abbeel
263 publications
Andreas Krause
182 publications
Yarin Gal
126 publications
Stefan Bauer
68 publications
Anirudh Goyal
61 publications
Vincent Fortuin
35 publications
Tamim Asfour
33 publications
John Schulman
31 publications
Alexandre Lacoste
28 publications

∙ 03/02/2023

Hallucinated Adversarial Control for Conservative Offline Policy Evaluation

We study the problem of conservative off-policy evaluation (COPE) where ...

0 Jonas Rothfuss, et al. ∙

∙ 11/14/2022

PAC-Bayesian Meta-Learning: From Theory to Practice

Meta-Learning aims to accelerate the learning on new tasks by acquiring ...

0 Jonas Rothfuss, et al. ∙

∙ 11/02/2022

Instance-Dependent Generalization Bounds via Optimal Transport

Existing generalization bounds fail to explain crucial factors that driv...

0 Songyan Hou, et al. ∙

∙ 10/27/2022

Lifelong Bandit Optimization: No Prior and No Regret

In practical applications, machine learning algorithms are often repeate...

0 Felix Schur, et al. ∙

∙ 10/24/2022

MARS: Meta-Learning as Score Matching in the Function Space

Meta-learning aims to extract useful inductive biases from a set of rela...

0 Krunoslav Lehman Pavasovic, et al. ∙

∙ 10/03/2022

Meta-Learning Priors for Safe Bayesian Optimization

In robotics, optimizing controller parameters under safety constraints i...

0 Jonas Rothfuss, et al. ∙

∙ 06/03/2022

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Learning causal structures from observation and experimentation is a cen...

18 Alexander Hägele, et al. ∙

∙ 05/25/2022

Amortized Inference for Causal Structure Learning

Learning causal structure poses a combinatorial search problem that typi...

0 Lars Lorch, et al. ∙

∙ 02/01/2022

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Obtaining reliable, adaptive confidence sets for prediction functions (h...

0 Parnian Kassraie, et al. ∙

∙ 06/14/2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Learning the causal structure that underlies data is a crucial step towa...

0 Yashas Annadani, et al. ∙

∙ 06/06/2021

Meta-Learning Reliable Priors in the Function Space

Meta-Learning promises to enable more data-efficient inference by harnes...

0 Jonas Rothfuss, et al. ∙

∙ 05/25/2021

DiBS: Differentiable Bayesian Structure Learning

Bayesian structure learning allows inferring Bayesian network structure ...

21 Lars Lorch, et al. ∙

∙ 03/10/2021

Robustness to Pruning Predicts Generalization in Deep Neural Networks

Existing generalization measures that aim to capture a model's simplicit...

0 Lorenz Kuhn, et al. ∙

∙ 02/13/2020

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Meta-learning can successfully acquire useful inductive biases from data...

3 Jonas Rothfuss, et al. ∙

∙ 07/21/2019

Noise Regularization for Conditional Density Estimation

Modelling statistical relationships beyond the conditional mean is cruci...

3 Jonas Rothfuss, et al. ∙

∙ 03/03/2019

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

Given a set of empirical observations, conditional density estimation ai...

0 Jonas Rothfuss, et al. ∙

∙ 10/16/2018

ProMP: Proximal Meta-Policy Search

Credit assignment in Meta-reinforcement learning (Meta-RL) is still poor...

0 Jonas Rothfuss, et al. ∙

∙ 09/14/2018

Model-Based Reinforcement Learning via Meta-Policy Optimization

Model-based reinforcement learning approaches carry the promise of being...

34 Ignasi Clavera, et al. ∙

∙ 07/02/2018

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets

We release two artificial datasets, Simulated Flying Shapes and Simulate...

0 Fabio Ferreira, et al. ∙

∙ 01/12/2018

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

We present a novel deep neural network architecture for representing rob...

0 Jonas Rothfuss, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world