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Found 25 papers, 13 papers with code
Differentially-Private Decision Trees and Provable Robustness to Data Poisoning
By leveraging the better privacy-utility trade-off of PrivaTree we are able to train decision trees with significantly better robustness against backdoor attacks compared to regular decision trees and with meaningful theoretical guarantees.
Optimal Decision Tree Policies for Markov Decision Processes
While there is generally a trade-off between the performance and interpretability of machine learning models, we find that OMDTs limited to a depth of 3 often perform close to the optimal limit.
SoK: Explainable Machine Learning for Computer Security Applications
The security literature sometimes also fails to disentangle the role of the various stakeholders, e. g., by providing explanations to model users and designers while also exposing them to adversaries.
ENCODE: Encoding NetFlows for Network Anomaly Detection
The first step for these machine learning pipelines is to pre-process the data before it is given to the machine learning algorithm.
Learning state machines via efficient hashing of future traces
In this paper we propose a method to learn state machines from data streams using the count-min-sketch data structure to reduce memory requirements.
Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster
In this work, we propose an approach that learns state machine models to model the runtime behaviour of a cloud environment that runs multiple microservice applications.
SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting
K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster interpretability.
Robust Attack Graph Generation
We present a method to learn automaton models that are more robust to input modifications.
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata
We present the efficient implementations of probabilistic deterministic finite automaton learning methods available in FlexFringe.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Robust Optimal Classification Trees Against Adversarial Examples
Decision trees are a popular choice of explainable model, but just like neural networks, they suffer from adversarial examples.
SAGE: Intrusion Alert-driven Attack Graph Extractor
We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge.
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation.
Efficient Training of Robust Decision Trees Against Adversarial Examples
We can use algorithms that take adversarial attacks into account to fit trees that are more robust.
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations.
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration.
Black-box Combinatorial Optimization using Models with Integer-valued Minima
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution.
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data
Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data.
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 (EDSM) algorithm for learning variants of finite state automata.
Learning Pairwise Disjoint Simple Languages from Positive Examples
A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples.
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
This paper focuses on detecting anomalies in a digital video broadcasting (DVB) system from providers' perspective.
Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models.
Complementing Model Learning with Mutation-Based Fuzzing
An ongoing challenge for learning algorithms formulated in the Minimally Adequate Teacher framework is to efficiently obtain counterexamples.
Software Engineering
Learning optimization models in the presence of unknown relations
Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver.
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3
This paper provides a gentle introduction to problem solving with the IDP3 system.
