1 citations · 2 across the 17 of their papers we have counts for
17 papers
Probabilistic Model Checking of Autoregressive Neural Sequence Models
Helge Spieker, Dennis Gross, Arnaud Gotlieb
Test-set accuracy is silent on two issues that matter when deploying autoregressive neural sequence models: how much probability mass the system under test (SUT) places on constrai…
Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oracle
Dennis Gross, Helge Spieker
Large language models (LLMs) are used as post hoc explainers of sequential decision-making policies, producing natural-language explanations of why an action was chosen. However, L…
Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
Dennis Gross, Quentin Mazouni, Helge Spieker +1
Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated testing methods target only sel…
COOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenance
Dennis Gross
Aging bridge networks require proactive, verifiable, and interpretable maintenance strategies, yet reinforcement learning (RL) policies trained solely on reward signals provide no…
COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management
Dennis Gross
Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstocking and life-threatening shortag…
Semi-supervised CAPP Transformer Learning via Pseudo-labeling
Dennis Gross, Helge Spieker, Arnaud Gotlieb +3
High-level Computer-Aided Process Planning (CAPP) generates manufacturing process plans from part specifications. It suffers from limited dataset availability in industry, reducing…
Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, Jørn Eirik Betten, Helge Spieker
The Rashomon effect describes the phenomenon where multiple models trained on the same data produce identical predictions while differing in which features they rely on internally.…
Verifying Memoryless Sequential Decision-making of Large Language Models
Dennis Gross, Helge Spieker, Arnaud Gotlieb
We introduce a tool for rigorous and automated verification of large language model (LLM)- based policies in memoryless sequential decision-making tasks. Given a Markov decision pr…
Bounded PCTL Model Checking of Large Language Model Outputs
Dennis Gross, Helge Spieker, Arnaud Gotlieb
In this paper, we introduce LLMCHECKER, a model-checking-based verification method to verify the probabilistic computation tree logic (PCTL) properties of an LLM text generation pr…
Reusable Test Suites for Reinforcement Learning
Jørn Eirik Betten, Quentin Mazouni, Dennis Gross +2
Reinforcement learning (RL) agents show great promise in solving sequential decision-making tasks. However, validating the reliability and performance of the agent policies' behavi…
Prompting for Performance: Exploring LLMs for Configuring Software
Helge Spieker, Théo Matricon, Nassim Belmecheri +7
Software systems usually provide numerous configuration options that can affect performance metrics such as execution time, memory usage, binary size, or bitrate. On the one hand,…
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking--a tec…
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Pruning neural networks (NNs) can streamline them but risks removing vital parameters from safe reinforcement learning (RL) policies. We introduce an interpretable RL method called…
Efficient Milling Quality Prediction with Explainable Machine Learning
Dennis Gross, Helge Spieker, Arnaud Gotlieb +2
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study empl…
Enhancing RL Safety with Counterfactual LLM Reasoning
Dennis Gross, Helge Spieker
Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-trai…
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
Dennis Gross, Helge Spieker, Arnaud Gotlieb +1
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes,…
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies
Dennis Gross, Helge Spieker
We introduce a method to verify stochastic reinforcement learning (RL) policies. This approach is compatible with any RL algorithm as long as the algorithm and its corresponding en…