4 citations · 8 across the 14 of their papers we have counts for
5 papers · 1 filter
Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning
Matthias Otth, Jonas Hübotter, Ido Hakimi +1
Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In thi…
Test-time Offline Reinforcement Learning on Goal-related Experience
Marco Bagatella, Mert Albaba, Jonas Hübotter +2
Foundation models compress a large amount of information in a single, large neural network, which can then be queried for individual tasks. There are strong parallels between this…
Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging
Ryo Bertolissi, Jonas Hübotter, Ido Hakimi +1
Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language mo…
Probabilistic Artificial Intelligence
Andreas Krause, Jonas Hübotter
Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intellige…
LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie +1
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challeng…