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Frederik Träuble

8 accepted papers

2023

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

Cian Eastwood, Andrei Liviu Nicolicioiu, Julius Von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi +1
ICLR 2023poster

In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativenes…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗
2023

Discrete Key-Value Bottleneck

Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio +1
ICML 2023poster

Deep neural networks perform well on classification tasks where data streams are i.i.d. and labeled data is abundant. Challenges emerge with non-stationary training data streams such as continual learning. One powerful approach that has addressed this challenge involves pre-training of large encoder…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2022

The Role of Pretrained Representations for the OOD Generalization of RL Agents

Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther +4
ICLR 2022poster

Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world…

Cited by 13Source ↗PDF ↗Scholar ↗
2022

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

Lukas Schott, Julius Von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge +3
ICLR 2022poster

An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning app…

Cited by 81Source ↗PDF ↗code ↗Scholar ↗
2021

Backward-Compatible Prediction Updates: A Probabilistic Approach

Frederik Träuble, Julius Von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter Vincent Gehler
NeurIPS 2021poster

When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models. While new improved models develop…

Cited by 20Source ↗PDF ↗Scholar ↗
2021

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio +2
ICLR 2021poster

Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we proposeCausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation envir…

Cited by 161Source ↗PDF ↗Scholar ↗
2021

On Disentangled Representations Learned from Correlated Data

Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal +2
ICML 2021oral

The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the…

Cited by 142Source ↗PDF ↗code ↗Scholar ↗
2021

On the Transfer of Disentangled Representations in Realistic Settings

Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther +2
ICLR 2021poster

Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, the…

Cited by 95Source ↗PDF ↗Scholar ↗