activity
20202026
most citedAdversarial Patch Camouflage against Aerial Detection

7 citations · 11 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Dennis Gross

Safe and interpretable sequential decision-making is critical in healthcare, yet reinforcement learning (RL) policies for sepsis treatment optimization remain opaque and difficult…

quant-ph2026

Formal Verification of Noisy Quantum Reinforcement Learning Policies

Dennis Gross

Quantum reinforcement learning (QRL) aims to use quantum effects to create sequential decision-making policies that achieve tasks more effectively than their classical counterparts…

cs.AI2025

Turn-based Multi-Agent Reinforcement Learning Model Checking

Dennis Gross

In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic mult…

cs.LG2022★ 1 cited

Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking

Dennis Gross, Thiago D. Simao, Nils Jansen +1

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adver…

cs.LG2022★ 3 cited

COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking

Dennis Gross, Nils Jansen, Sebastian Junges +1

This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the proba…

cs.CV2020★ 7 cited

Adversarial Patch Camouflage against Aerial Detection

Ajaya Adhikari, Richard den Hollander, Ioannis Tolios +9

Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military a…

cs.LG2020

Robustness Verification for Classifier Ensembles

Dennis Gross, Nils Jansen, Guillermo A. Pérez +1

We give a formal verification procedure that decides whether a classifier ensemble is robust against arbitrary randomized attacks. Such attacks consist of a set of deterministic at…