572 citations · 644 across the 10 of their papers we have counts for
11 papers
Simulation-Free Learning of Population Dynamics with Wasserstein Lagrangian Residuals
Fedor Sergeev, Markus Heinonen, Daniel Waxman +4
The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this evolution from unpaired snapsh…
A Computationally Feasible Framework for Causal Probabilistic Explanation
Rafal Urbaniak, Sam Witty, Daniel Waxman +7
Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into tw…
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Markus Heinonen, Yair Shenfeld, Ricardo Baptista +4
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distribu…
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
Daniel Waxman, Dmitry Batenkov, John Feser +4
State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning.…
Pact: A Choreographic Language for Agentic Ecosystems
Kiran Gopinathan, Jack Feser, Michelangelo Naim +2
Recent advances in large language models have led to the rise of software systems (i.e. agents) that execute with increasing autonomy on behalf of users in open, multi-party settin…
NeuroAI for AI Safety
Patrick Mineault, Niccolò Zanichelli, Joanne Zichen Peng +12
As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general…