5 citations · 5 across the 6 of their papers we have counts for
6 papers
Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms
Vashist Avadhanula, Omar Abdul Baki, Hamsa Bastani +17
We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models…
Robust and fair work allocation
Amine Allouah, Christian Kroer, Xuan Zhang +6
In today's digital world, interaction with online platforms is ubiquitous, and thus content moderation is important for protecting users from content that do not comply with pre-es…
QUEST: Queue Simulation for Content Moderation at Scale
Rahul Makhijani, Parikshit Shah, Vashist Avadhanula +3
Moderating content in social media platforms is a formidable challenge due to the unprecedented scale of such systems, which typically handle billions of posts per day. Some of the…
Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising
Vashist Avadhanula, Riccardo Colini-Baldeschi, Stefano Leonardi +2
We study the problem of an online advertising system that wants to optimally spend an advertiser's given budget for a campaign across multiple platforms, without knowing the value…
Multi-armed Bandits with Cost Subsidy
Deeksha Sinha, Karthik Abinav Sankararama, Abbas Kazerouni +1
In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to…
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Samuel Daulton, Shaun Singh, Vashist Avadhanula +2
Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Rea…