Survey on Fair Reinforcement Learning: Theory and Practice

Autor: Gajane, Pratik, Saxena, Akrati, Tavakol, Maryam, Fletcher, George, Pechenizkiy, Mykola

Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-supervised

Externí odkaz: http://arxiv.org/abs/2205.10032

Distantly Supervised Question Parsing

Autor: Zafar, Hamid, Tavakol, Maryam, Lehmann, Jens

The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents of the question and the corre

Externí odkaz: http://arxiv.org/abs/1909.12566

Matrix factorization with denoising autoencoders for prediction of drug–target interactions.

Autor: Sajadi, Seyedeh Zahra, Zare Chahooki, Mohammad Ali, Tavakol, Maryam, Gharaghani, Sajjad

Publikováno v: Molecular Diversity; Jun2023, Vol. 27 Issue 3, p1333-1343, 11p

HyperUCB:Hyperparameter optimization using contextual bandits

Autor: Tavakol, Maryam, Mair, Sebastian, Morik, Katharina

Publikováno v: Tavakol, M, Mair, S & Morik, K 2020, HyperUCB : Hyperparameter optimization using contextual bandits . in P Cellier & K Driessens (eds), Machine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I . vol. 1, Communications in Computer and Information Science, vol. 1167, Springer Nature AG, Cham, pp. 44-50, 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases-2019, Wurzburg, Germany, 16.09.19 . https://doi.org/10.1007/978-3-030-43823-4_4

Setting the optimal hyperparameters of a learning algorithm is a crucial task. Common approaches such as a grid search over the hyperparameter space or randomly sampling hyperparameters require many configurations to be evaluated in order to perform

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3697::48027048cfbc202f4137cf2a4f8137f6
http://www.scopus.com/inward/record.url?scp=85083719265&partnerID=8YFLogxK

MDP-based itinerary recommendation using geo-tagged social media

Autor: Gaonkar, Radhika, Tavakol, Maryam, Brefeld, Ulf

Publikováno v: Gaonkar, R, Tavakol, M & Brefeld, U 2018, MDP-based itinerary recommendation using geo-tagged social media . in W Duivesteijn, A Siebes & A Ukkonen (eds), Advances in Intelligent Data Analysis XVII-17th International Symposium, IDA 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. 11191, Springer Nature, Basel, pp. 111-123, 17th International Symposium on Intelligent Data Analysis-IDA 2018, ‘s-Hertogenbosch, Netherlands, 24.10.18 . DOI: 10.1007/978-3-030-01768-2_10
Gaonkar, R, Tavakol, M & Brefeld, U 2018, MDP-based itinerary recommendation using geo-tagged social media . in W Duivesteijn, A Siebes & A Ukkonen (eds), Advances in Intelligent Data Analysis XVII-17th International Symposium, IDA 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. 11191, Springer Nature AG, Basel, pp. 111-123, 17th International Symposium on Intelligent Data Analysis-IDA 2018, ‘s-Hertogenbosch, Netherlands, 24.10.18 . https://doi.org/10.1007/978-3-030-01768-2_10

Planning vacations is a complex decision problem. Many variables like the place(s) to visit, how many days to stay, the duration at each location, and the overall travel budget need to be controlled and arranged by the user. Automatically recommendin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::11f42ef60dfa01e5d4fe57a97aa09971
http://www.scopus.com/inward/record.url?scp=85055718851&partnerID=8YFLogxK