no code implementations • 10 Aug 2021 • George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis, Fragkiskos D. Malliaros
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem.
no code implementations • 27 Aug 2020 • Yang Qiu, Nikolaos Tziortziotis, Martial Hue, Michalis Vazirgiannis
Online display advertising is growing rapidly in recent years thanks to the automation of the ad buying process.
no code implementations • 6 Apr 2019 • Nikolaos Tziortziotis, Christos Dimitrakakis, Michalis Vazirgiannis
We introduce Bayesian least-squares policy iteration (BLSPI), an off-policy, model-free, policy iteration algorithm that uses the Bayesian least-squares temporal-difference (BLSTD) learning algorithm to evaluate policies.
1 code implementation • WS 2018 • Konstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential.
no code implementations • 23 Apr 2017 • Guillaume Salha, Nikolaos Tziortziotis, Michalis Vazirgiannis
This paper examines the problem of adaptive influence maximization in social networks.
Social and Information Networks
no code implementations • 22 Aug 2014 • Nikolaos Tziortziotis, Georgios Papagiannis, Konstantinos Blekas
This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem.
no code implementations • 8 May 2013 • Nikolaos Tziortziotis, Christos Dimitrakakis, Konstantinos Blekas
This paper proposes an online tree-based Bayesian approach for reinforcement learning.
no code implementations • 27 Mar 2013 • Christos Dimitrakakis, Nikolaos Tziortziotis
This paper introduces a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC).