2 citations · 3 across the 12 of their papers we have counts for
5 papers · 1 filter
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim +3
Multi-sample reasoning measures candidate coverage, the probability that at least one of candidates is correct. However, minimizing the average loss of individual attempts does…
A Generalization of Amari's Bayesian Duality
Mohammad Emtiyaz Khan, Thomas Möllenhoff
Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not received as much attention. We co…
Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Christopher J. Anders, Henrique Da Silva Gameiro, Nico Daheim +1
One way to understand LLM behavior is to trace its output back to the training data. Two types of measures are commonly used for output tracing: data-similarity and data-influence.…
Joint Model and Data Sparsification via the Marginal Likelihood
Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2
Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic rel…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…