research
∙
05/24/2023
Differentially-Private Decision Trees with Probabilistic Robustness to Data Poisoning
Decision trees are interpretable models that are well-suited to non-line...
share
research
∙
01/30/2023
Optimal Decision Tree Policies for Markov Decision Processes
Interpretability of reinforcement learning policies is essential for man...
research
∙
08/22/2022
SoK: Explainable Machine Learning for Computer Security Applications
Explainable Artificial Intelligence (XAI) is a promising solution to imp...
research
∙
07/08/2022
Encoding NetFlows for State-Machine Learning
NetFlow data is a well-known network log format used by many network ana...
research
∙
07/04/2022
Learning state machines via efficient hashing of future traces
State machines are popular models to model and visualize discrete system...
research
∙
06/28/2022
Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster
These days more companies are shifting towards using cloud environments ...
research
∙
06/24/2022
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
Sequence clustering in a streaming environment is challenging because it...
research
∙
06/15/2022
Robust Attack Graph Generation
We present a method to learn automaton models that are more robust to in...
research
∙
03/28/2022
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata
We present the efficient implementations of probabilistic deterministic ...
research
∙
01/25/2022
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
This paper reports on the first international competition on AI for the ...
research
∙
09/08/2021
Robust Optimal Classification Trees Against Adversarial Examples
Decision trees are a popular choice of explainable model, but just like ...
research
∙
07/06/2021
SAGE: Intrusion Alert-driven Attack Graph Extractor
Attack graphs (AG) are used to assess pathways availed by cyber adversar...
research
∙
06/08/2021
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Surrogate algorithms such as Bayesian optimisation are especially design...
research
∙
12/18/2020
Efficient Training of Robust Decision Trees Against Adversarial Examples
In the present day we use machine learning for sensitive tasks that requ...
research
∙
11/06/2020
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
One method to solve expensive black-box optimization problems is to use ...
research
∙
06/08/2020
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
A challenging problem in both engineering and computer science is that o...
research
∙
11/20/2019
Black-box Combinatorial Optimization using Models with Integer-valued Minima
When a black-box optimization objective can only be evaluated with costl...
research
∙
10/29/2019
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data
Imitation learning provides a way to automatically construct a controlle...
research
∙
04/02/2019
MalPaCA: Malware Packet Sequence Clustering and Analysis
Malware family characterization is a challenging problem because ground-...
research
∙
07/28/2017
Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
We present an interactive version of an evidence-driven state-merging (E...
research
∙
05/24/2017
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
This paper focuses on detecting anomalies in a digital video broadcastin...
research
∙
11/21/2016
Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models. Interpretabilit...
research
∙
01/06/2014
