1 citations · 1 across the 5 of their papers we have counts for
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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Patrik Reizinger, Bálint Mucsányi, Siyuan Guo +3
Self-supervised feature learning and pretraining methods in reinforcement learning (RL) often rely on information-theoretic principles, termed mutual information skill learning (MI…
Logit Reweighting for Topic-Focused Summarization
Joschka Braun, Bálint Mucsányi, Seyed Ali Bahrainian
Generating abstractive summaries that adhere to a specific topic remains a significant challenge for language models. While standard approaches, such as fine-tuning, are resource-i…
Kronecker-factored Approximate Curvature (KFAC) From Scratch
Felix Dangel, Bálint Mucsányi, Tobias Weber +1
Kronecker-factored approximate curvature (KFAC) is arguably one of the most prominent curvature approximations in deep learning. Its applications range from optimization to Bayesia…
Geometric Gaussian Approximations of Probability Distributions
Nathaël Da Costa, Bálint Mucsányi, Philipp Hennig
Approximating complex probability distributions, such as Bayesian posterior distributions, is of central interest in many applications. We study the expressivity of geometric Gauss…
Rethinking Approximate Gaussian Inference in Classification
Bálint Mucsányi, Nathaël Da Costa, Philipp Hennig
In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capt…