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Found 19 papers, 11 papers with code
Causally Abstracted Multi-armed Bandits
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems.
Interventionally Consistent Surrogates for Agent-based Simulators
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.
Causal Optimal Transport of Abstractions
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them.
Quantifying Consistency and Information Loss for Causal Abstraction Learning
However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models.
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution.
Towards Computing an Optimal Abstraction for Structural Causal Models
Working with causal models at different levels of abstraction is an important feature of science.
Abstraction between Structural Causal Models: A Review of Definitions and Properties
Structural causal models (SCMs) are a widespread formalism to deal with causal systems.
Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents
In this paper, we propose a formalization of the process of exploitation of SQL injection vulnerabilities.
Stack-based Buffer Overflow Detection using Recurrent Neural Networks
Moreover, we subscribe to the hypothesis that code may be treated as natural language, and thus we process assembly code using standard architectures commonly employed in natural language processing.
Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents such as terrorism, general criminal offences, or even domestic violence.
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation.
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge
In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them.
Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks
In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon.
Towards Further Understanding of Sparse Filtering via Information Bottleneck
In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in the study of the information bottleneck (IB).
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
In this paper we offer a preliminary study of the application of Bayesian coresets to network security data.
Counterfactually Fair Prediction Using Multiple Causal Models
In this paper we study the problem of making predictions using multiple structural casual models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness.
Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation
In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness.
On the Use of Sparse Filtering for Covariate Shift Adaptation
We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation.
Towards Understanding Sparse Filtering: A Theoretical Perspective
In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning.
