45 citations · 106 across the 11 of their papers we have counts for
12 papers
Scaling of Capability and Efficiency at Inference Time in Large Reasoning Models
Moritz Laber, Zohair Shafi, Germans Savcisens +6
Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem correctly, whereas efficien…
An analytical model of active inference in the Iterated Prisoner's Dilemma
Daphne Demekas, Conor Heins, Brennan Klein
This paper addresses a mathematically tractable model of the Prisoner's Dilemma using the framework of active inference. In this work, we design pairs of Bayesian agents that are t…
Spatial scales of COVID-19 transmission in Mexico
Brennan Klein, Harrison Hartle, Munik Shrestha +13
During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hie…
Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic
Brennan Klein, Timothy LaRock, Stefan McCabe +13
The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and…
BiaScope: Visual Unfairness Diagnosis for Graph Embeddings
Agapi Rissaki, Bruno Scarone, David Liu +4
The issue of bias (i.e., systematic unfairness) in machine learning models has recently attracted the attention of both researchers and practitioners. For the graph mining communit…
Spin glass systems as collective active inference
Conor Heins, Brennan Klein, Daphne Demekas +2
An open question in the study of emergent behaviour in multi-agent Bayesian systems is the relationship, if any, between individual and collective inference. In this paper we explo…
pymdp: A Python library for active inference in discrete state spaces
Conor Heins, Beren Millidge, Daphne Demekas +4
Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inferenc…
netrd: A library for network reconstruction and graph distances
Stefan McCabe, Leo Torres, Timothy LaRock +4
Over the last two decades, alongside the increased availability of large network datasets, we have witnessed the rapid rise of network science. For many systems, however, the data…
Network comparison and the within-ensemble graph distance
Harrison Hartle, Brennan Klein, Stefan McCabe +4
Quantifying the differences between networks is a challenging and ever-present problem in network science. In recent years a multitude of diverse, ad hoc solutions to this problem…
Finding the right scale of a network: Efficient identification of causal emergence through spectral clustering
Ross Griebenow, Brennan Klein, Erik Hoel
All networks can be analyzed at multiple scales. A higher scale of a network is made up of macro-nodes: subgraphs that have been grouped into individual nodes. Recasting a network…
The emergence of informative higher scales in complex networks
Brennan Klein, Erik Hoel
The connectivity of a network contains information about the relationships between nodes, which can denote interactions, associations, or dependencies. We show that this informatio…
Optimal design of experiments to identify latent behavioral types
Stefano Balietti, Brennan Klein, Christoph Riedl
Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designi…