← Search

Peter Vincent Gehler

6 accepted papers

2022

Assaying Out-Of-Distribution Generalization in Transfer Learning

NeurIPS 2022accept

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the sa…

2022

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

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 13SourcePDFScholar
2022

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

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…

2022

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

ICLR 2022poster

Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to…

Cited by 41SourcePDFScholar
2021

Backward-Compatible Prediction Updates: A Probabilistic Approach

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 20SourcePDFScholar
2021

Dynamic Inference with Neural Interpreters

NeurIPS 2021poster

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning…

Cited by 36SourcePDFScholar