9 citations · 15 across the 6 of their papers we have counts for
6 papers
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data is ubiquitous in the empirical sciences and most commonly analyzed with generalized linear mixed-effects models (GLMMs), which have interpretable parameters. Baye…
Measuring What AI Systems Might Do: Towards A Measurement Science in AI
Konstantinos Voudouris, Mirko Thalmann, Alex Kipnis +2
Scientists, policy-makers, business leaders, and members of the public care about what modern artificial intelligence systems are disposed to do. Yet terms such as capabilities, pr…
metabeta -- A fast neural model for Bayesian mixed-effects regression
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
metabench -- A Sparse Benchmark of Reasoning and Knowledge in Large Language Models
Alex Kipnis, Konstantinos Voudouris, Luca M. Schulze Buschoff +1
Large Language Models (LLMs) vary in their abilities on a range of tasks. Initiatives such as the Open LLM Leaderboard aim to quantify these differences with several large benchmar…
Statistical inference on representational geometries
Heiko H. Schütt, Alexander D. Kipnis, Jörn Diedrichsen +1
Neuroscience has recently made much progress, expanding the complexity of both neural-activity measurements and brain-computational models. However, we lack robust methods for conn…