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06/11/2023
Improving the Validity of Decision Trees as Explanations
In classification and forecasting with tabular data, one often utilizes ...
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05/26/2023
NASimEmu: Network Attack Simulator Emulator for Training Agents Generalizing to Novel Scenarios
Current frameworks for training offensive penetration testing agents wit...
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05/08/2023
Is AUC the best measure for practical comparison of anomaly detectors?
The area under receiver operating characteristics (AUC) is the standard ...
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11/07/2022
Using Set Covering to Generate Databases for Holistic Steganalysis
Within an operational framework, covers used by a steganographer are lik...
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09/12/2022
A Differentiable Loss Function for Learning Heuristics in A*
Optimization of heuristic functions for the A* algorithm, realized by de...
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08/04/2022
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
Learning from raw data input, thus limiting the need for feature enginee...
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12/03/2021
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Learning a well-informed heuristic function for hard task planning domai...
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10/10/2021
Fitting large mixture models using stochastic component selection
Traditional methods for unsupervised learning of finite mixture models r...
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08/17/2021
When Should You Defend Your Classifier – A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Adversarial machine learning, i.e., increasing the robustness of machine...
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05/19/2021
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Learning from raw data input, thus limiting the need for manual feature ...
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04/19/2021
Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
Even though machine learning algorithms already play a significant role ...
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12/11/2020
Comparison of Anomaly Detectors: Context Matters
Deep generative models are challenging the classical methods in the fiel...
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09/25/2020
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks
We present a novel deep reinforcement learning framework for solving rel...
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06/02/2020
Neural Power Units
Conventional Neural Networks can approximate simple arithmetic operation...
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05/04/2020
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
In this work, we propose Sum-Product-Transform Networks (SPTN), an exten...
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02/25/2020
General Framework for Binary Classification on Top Samples
Many binary classification problems minimize misclassification above (or...
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02/10/2020
Nested Multiple Instance Learning in Modelling of HTTP network traffic
In many interesting cases, the application of machine learning is hinder...
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12/02/2019
Rodent: Relevance determination in ODE
From a set of observed trajectories of a partially observed system, we a...
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11/20/2019
Deep Reinforcement Learning with Explicitly Represented Knowledge and Variable State and Action Spaces
We focus on a class of real-world domains, where gathering hierarchical ...
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09/05/2019
Classification with Costly Features as a Sequential Decision-Making Problem
This work focuses on a specific classification problem, where the inform...
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06/21/2019
Joint Detection of Malicious Domains and Infected Clients
Detection of malware-infected computers and detection of malicious web d...
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06/03/2019
Approximation capability of neural networks on spaces of probability measures and tree-structured domains
This paper extends the proof of density of neural networks in the space ...
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05/28/2019
Anomaly scores for generative models
Reconstruction error is a prevalent score used to identify anomalous sam...
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07/13/2018
Are generative deep models for novelty detection truly better?
Many deep models have been recently proposed for anomaly detection. This...
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06/30/2018
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems
Machine Learning models incorporating multiple layered learning networks...
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06/30/2018
Algorithms for solving optimization problems arising from deep neural net models: smooth problems
Machine Learning models incorporating multiple layered learning networks...
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11/20/2017
Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for...
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09/23/2016