1 citations · 3 across the 7 of their papers we have counts for
7 papers
Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Study
Michael Walters, Rafael Kaufmann, Justice Sefas +1
We investigate the Free Energy Principle as a foundation for measuring risk in agentic and multi-agent systems. From these principles we introduce a Cumulative Risk Exposure metric…
On Weighted Trigonometric Regression for Suboptimal Designs in Circadian Biology Studies
Michael Gorczyca, Justice Sefas
Studies in circadian biology often use trigonometric regression to model phenomena over time. Ideally, protocols in these studies would collect samples at evenly distributed and eq…
A Diffusion-Model of Joint Interactive Navigation
Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu +8
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios i…
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg +8
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We invest…
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington +8
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.…
Conditional Permutation Invariant Flows
Berend Zwartsenberg, Adam Ścibior, Matthew Niedoba +7
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation…
Critic Sequential Monte Carlo
Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas +6
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic…