19 citations · 23 across the 4 of their papers we have counts for
6 papers
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training
Athirai A. Irissappane, Hanfei Yu, Yankun Shen +2
Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of…
EasyRL: A Simple and Extensible Reinforcement Learning Framework
Neil Hulbert, Sam Spillers, Brandon Francis +7
In recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To thi…
GANs for Semi-Supervised Opinion Spam Detection
Gray Stanton, Athirai A. Irissappane
Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A…
Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
Athirai A. Irissappane, Frans A. Oliehoek, Jie Zhang
In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors). Partially Observable Markov Decision Processes (POMDPs) have sho…