Jonas Rothfuss | DeepAI

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∙ 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. ∙

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