Search Results for author:
Found 11 papers, 8 papers with code
Manipulating hidden-Markov-model inferences by corrupting batch data
This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.
Simulation Based Bayesian Optimization
BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
Adversarial attacks against Bayesian forecasting dynamic models
The last decade has seen the rise of Adversarial Machine Learning (AML).
Data sharing games
Data sharing issues pervade online social and economic environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Protecting Classifiers From Attacks. A Bayesian Approach
Classification problems in security settings are usually modeled as confrontations in which an adversary tries to fool a classifier manipulating the covariates of instances to obtain a benefit.
Adversarial Machine Learning: Bayesian Perspectives
Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems.
Opponent Aware Reinforcement Learning
We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL).
Gradient Methods for Solving Stackelberg Games
In AML, decisions are made by algorithms and are usually continuous and high dimensional, e. g. choosing the weights of a neural network.
Reinforcement Learning under Threats
In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process.
Adversarial classification: An adversarial risk analysis approach
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit.
Large Scale Automated Forecasting for Monitoring Network Safety and Security
Real time large scale streaming data pose major challenges to forecasting, in particular defying the presence of human experts to perform the corresponding analysis.
