Search Results for author: Bibek Paudel

Found 8 papers, 1 papers with code

Towards Automatic Bias Detection in Knowledge Graphs

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.

Bias Detection Fairness +2

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

no code implementations18 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.

Adversarial Learning for Debiasing Knowledge Graph Embeddings

no code implementations29 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.

Knowledge Graph Embeddings Knowledge Graphs +1

A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records

no code implementations23 Jun 2020 Leopold Franz, Yash Raj Shrestha, Bibek Paudel

Second, machine learning algorithms that predict multiple disease diagnosis categories simultaneously remain underdeveloped.

BIG-bench Machine Learning Decision Making

Cross-Cutting Political Awareness through Diverse News Recommendations

no code implementations3 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.

Recommendation Systems

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

no code implementations21 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.

Entity Embeddings Knowledge Graphs +1

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

no code implementations12 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.

Knowledge Graph Embedding Knowledge Graphs +1

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