1k citations · 2.1k across the 17 of their papers we have counts for
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Learning Commute-Time-Preserving World Models for Planning
Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch +1
World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning can benefit from latent rep…
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance
Fabian A. Mikulasch, Friedemann Zenke
Self-supervised learning (SSL) by predicting in latent space, without generating the input data itself, learns highly abstract, useful representations. Intuitively, this success is…
Understanding Self-Supervised Learning via Latent Distribution Matching
Fabian A Mikulasch, Friedemann Zenke
Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework that explains the diverse exis…
Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction
Michael Hauri, Friedemann Zenke
Model-based reinforcement learning (MBRL) agents operating in high-dimensional observation spaces, such as Dreamer, rely on learning abstract representations for effective planning…