1 code implementation • Findings (EMNLP) 2021 • Daphna Keidar, Mian Zhong, Ce Zhang, Yash Raj Shrestha, Bibek Paudel
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident.
no code implementations • 18 Feb 2021 • Bibek Paudel, Abraham Bernstein
Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users.
no code implementations • 29 Jun 2020 • Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, Bibek Paudel
As a second step, we explore gender bias in KGE, and a careful examination of popular KGE algorithms suggest that sensitive attribute like the gender of a person can be predicted from the embedding.
no code implementations • 23 Jun 2020 • Leopold Franz, Yash Raj Shrestha, Bibek Paudel
Second, machine learning algorithms that predict multiple disease diagnosis categories simultaneously remain underdeveloped.
no code implementations • 3 Sep 2019 • Bibek Paudel, Abraham Bernstein
The suggestions generated by most existing recommender systems are known to suffer from a lack of diversity, and other issues like popularity bias.
no code implementations • 21 Mar 2019 • Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei zhang, Abraham Bernstein, Huajun Chen
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
no code implementations • 12 Mar 2019 • Wen Zhang, Bibek Paudel, Wei zhang, Abraham Bernstein, Huajun Chen
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
no code implementations • 29 Dec 2018 • Bibek Paudel, Sandro Luck, Abraham Bernstein
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems.