activity
20122026
most citedBayesian Exploration Networks

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2026

MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning

Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de Witt

World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capture the information needed f…

cs.MA2026

Modelling Opinion Dynamics at Scale with Deep MARL

Lukas Seier, Brandon Kaplowitz, Sebastian Towers +2

Modelling opinion dynamics typically relies on hand-crafted local interaction rules to study emergent macroscopic phenomena such as consensus and polarisation. In contrast, multi-a…

cs.AI2026

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

Aaron Rose, Carissa Cullen, Sahar Abdelnabi +3

Non-verbalised or secret collusion may impose a ceiling on transcript-level monitoring in multi-agent deployments. Internal model activations are known to provide an additional mon…

econ.GN2025

Reinforcement Learning and Consumption-Savings Behavior

Brandon Kaplowitz, Brandon Gary Kaplowitz

This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during economic downturns. I develop a model where…

cs.LG2023★ 1 cited

Bayesian Exploration Networks

Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt +1

Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an explora…

q-fin.PR2012

Stochastic PDEs and Quantitative Finance: The Black-Scholes-Merton Model of Options Pricing and Riskless Trading

Brandon Kaplowitz, Siddharth G. Reddy

Differential equations can be used to construct predictive models of a diverse set of real-world phenomena like heat transfer, predator-prey interactions, and missile tracking. In…