32 citations · 106 across the 33 of their papers we have counts for
23 papers · 1 filter
Intersectional Fairness via Mixed-Integer Optimization
Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2
The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks,…
Distillation of a tractable model from the VQ-VAE
Armin Hadžić, Milan Papez, Tomáš Pevný
Deep generative models with discrete latent space, such as the Vector-Quantized Variational Autoencoder (VQ-VAE), offer excellent data generation capabilities, but, due to the larg…
Sparse Probabilistic Graph Circuits
Martin Rektoris, Milan Papež, Václav Šmídl +1
Deep generative models (DGMs) for graphs achieve impressively high expressive power thanks to very efficient and scalable neural networks. However, these networks contain non-linea…
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
Milan Papež, Martin Rektoris, Václav Šmídl +1
Deep generative models (DGMs) have recently demonstrated remarkable success in capturing complex probability distributions over graphs. Although their excellent performance is attr…
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Milan Papež, Martin Rektoris, Václav Šmídl +1
Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer…
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Milan Papež, Martin Rektoris, Tomáš Pevný +1
Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models…
Malicious Internet Entity Detection Using Local Graph Inference
Simon Mandlik, Tomas Pevny, Vaclav Smidl +1
Detection of malicious behavior in a large network is a challenging problem for machine learning in computer security, since it requires a model with high expressive power and scal…
Leveraging Data Geometry to Mitigate CSM in Steganalysis
Rony Abecidan, Vincent Itier, Jérémie Boulanger +2
In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train stegana…
Improving the Validity of Decision Trees as Explanations
Jiri Nemecek, Tomas Pevny, Jakub Marecek
In classification and forecasting with tabular data, one often utilizes tree-based models. Those can be competitive with deep neural networks on tabular data and, under some condit…
Is AUC the best measure for practical comparison of anomaly detectors?
Vít Škvára, Tomáš Pevný, Václav Šmídl
The area under receiver operating characteristics (AUC) is the standard measure for comparison of anomaly detectors. Its advantage is in providing a scalar number that allows a nat…
A Differentiable Loss Function for Learning Heuristics in A*
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values. T…
Fitting large mixture models using stochastic component selection
Milan Papež, Tomáš Pevný, Václav Šmídl
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This becomes computationally prohibitive…
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Maximilian Samsinger, Florian Merkle, Pascal Schöttle +1
Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly propose…
Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks
Simon Mandlik, Tomas Pevny
Even though machine learning algorithms already play a significant role in data science, many current methods pose unrealistic assumptions on input data. The application of such me…
Comparison of Anomaly Detectors: Context Matters
Vít Škvára, Jan Franců, Matěj Zorek +2
Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every new method provides evidence of outperforming its predecessors, often…
Neural Power Units
Niklas Heim, Tomáš Pevný, Václav Šmídl
Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Unit…
General Framework for Binary Classification on Top Samples
Lukáš Adam, Václav Mácha, Václav Šmídl +1
Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top or hypothesis testing m…
Classification with Costly Features in Hierarchical Deep Sets
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
Classification with Costly Features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features a…
Classification with Costly Features as a Sequential Decision-Making Problem
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample…
Joint Detection of Malicious Domains and Infected Clients
Paul Prasse, Rene Knaebel, Lukas Machlica +2
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps,…
Approximation capability of neural networks on spaces of probability measures and tree-structured domains
Tomas Pevny, Vojtech Kovarik
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability…
Are generative deep models for novelty detection truly better?
Vít Škvára, Tomáš Pevný, Václav Šmídl
Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an…
Using Neural Network Formalism to Solve Multiple-Instance Problems
Tomas Pevny, Petr Somol
Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Mul…