| 1. |
- Mitrokotsa, Aikaterini, 1978, et al.
(author)
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Intrusion Detection Using Cost-Sensitive Classification
- 2007
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In: Proceedings of the 3rd European Conference on Computer Network Defense (EC2ND 2007). - Boston, MA : Springer US. - 9780387855547 ; 30, s. 35-47
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Conference paper (peer-reviewed)abstract
- Intrusion Detection is an invaluable part of computer networks defense. An important consideration is the fact that raising false alarms carries a significantly lower cost than not detecting attacks. For this reason, we examine how cost-sensitive classification methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions, cost matrices and different classification models, in terms of expected cost, as well as detection and false alarm rates. We find that even under unfavourable conditions, cost-sensitive classification can improve performance significantly, if only slightly.
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| 2. |
- Androulakis, Emmanouil, et al.
(author)
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Generalised entropy MDPs and Minimax Regret
- 2014
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In: NIPS 2014, From bad models to good policies workshop..
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Conference paper (peer-reviewed)abstract
- Bayesian methods suffer from the problem of how to specify prior beliefs. One interesting idea is to consider worst-case priors. This requires solving a stochastic zero-sum game. In this paper, we extend well-known results from bandit theory in order to discover minimax-Bayes policies and discuss when they are practical.
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| 3. |
- Athanasopoulos, Andreas, et al.
(author)
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Approximate Inference for the Bayesian Fairness Framework
- 2023
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In: CEUR Workshop Proceedings. - 1613-0073. ; 3442
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Conference paper (peer-reviewed)abstract
- As the impact of Artificial Intelligence systems and applications on everyday life increases, algorithmic fairness undoubtedly constitutes one of the major problems in our modern society. In the current paper, we extend the work of Dimitrakakis et al. on Bayesian fairness [1] that incorporates models uncertainty to achieve fairness, proposing a practical algorithm with the aim to scale the framework for a broader range of applications. We begin by applying the bootstrap technique as a scalable alternative to approximate the posterior distribution of parameters of the fully Bayesian viewpoint. To make the Bayesian fairness framework applicable to more general data settings, we define an empirical formulation suitable for the continuous case. We experimentally demonstrate the potential of the framework from an extensive evaluation study on a real dataset and different decision settings.
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| 4. |
- Brunetta, Carlo, 1992, et al.
(author)
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A Differentially Private Encryption Scheme
- 2017
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 10599 LNCS, s. 309-326
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Conference paper (peer-reviewed)abstract
- Encrypting data with a semantically secure cryptosystem guarantees that nothing is learned about the plaintext from the ciphertext. However, querying a database about individuals or requesting for summary statistics can leak information. Differential privacy (DP) offers a formal framework to bound the amount of information that an adversary can discover from a database with private data, when statistical findings of the stored data are communicated to an untrusted party. Although both encryption schemes and differential private mechanisms can provide important privacy guarantees, when employed in isolation they do not guarantee full privacy-preservation. This paper investigates how to efficiently combine DP and an encryption scheme to prevent leakage of information. More precisely, we introduce and instantiate differentially private encryption schemes that provide both DP and confidentiality. Our contributions are five-fold, we: (i) define an encryption scheme that is not correct with some probability i.e., an -correct encryption scheme and we prove that it satisfies the DP definition; (ii) prove that combining DP and encryption, is equivalent to using an -correct encryption scheme and provide a construction to build one from the other; (iii) prove that an encryption scheme that belongs in the DP-then-Encrypt class is at least as computationally secure as the original base encryption scheme; (iv) provide an -correct encryption scheme that achieves both requirements (i.e., DP and confidentiality) and relies on Dijk et al.’s homomorphic encryption scheme (EUROCRYPT 2010); and (v) perform some statistical experiments on our encryption scheme in order to empirically check the correctness of the theoretical results.
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| 5. |
- Buning, Thomas Kleine, et al.
(author)
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Interactive Inverse Reinforcement Learning for Cooperative Games
- 2022
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In: Proceedings of Machine Learning Research. - 2640-3498. ; 162, s. 2413-2413
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Conference paper (peer-reviewed)abstract
- We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible and so that the joint policy is as close to optimal as possible? We analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.
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| 6. |
- Dimitrakakis, Christos, 1975, et al.
(author)
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ABC Reinforcement Learning
- 2013
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In: JMLR W&CP (ICML 2013). ; 28:3, s. 684-692
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Conference paper (peer-reviewed)abstract
- We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use of any Bayesian reinforcement learning technique in this case. It can be seen as an extension of simulation methods to both planning and inference. We experimentally demonstrate the potential of this approach in a comparison with LSPI. Finally, we introduce a theorem showing that ABC is sound.
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| 7. |
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| 8. |
- Dimitrakakis, Christos, 1975, et al.
(author)
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Bayesian Fairness
- 2019
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In: THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. - 9781577358091 ; , s. 509-516
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Conference paper (peer-reviewed)abstract
- We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in (Kleinberg, Mullainathan, and Raghavan 2016), we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.
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