22 citations · 28 across the 10 of their papers we have counts for
11 papers
Fully Dynamic Online Selection through Online Contention Resolution Schemes
Vashist Avadhanula, Andrea Celli, Riccardo Colini-Baldeschi +2
We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms, and st…
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…
Top Ranking for Multi-Armed Bandit with Noisy Evaluations
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric +1
We consider a multi-armed bandit setting where, at the beginning of each round, the learner receives noisy independent, and possibly biased, \emph{evaluations} of the true reward o…
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…
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
Priyank Agrawal, Theja Tulabandhula, Vashist Avadhanula
In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subs…
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…
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
Shipra Agrawal, Vashist Avadhanula, Vineet Goyal +1
We consider a dynamic assortment selection problem, where in every round the retailer offers a subset (assortment) of substitutable products to a consumer, who selects one of t…
Thompson Sampling for the MNL-Bandit
Shipra Agrawal, Vashist Avadhanula, Vineet Goyal +1
We consider a sequential subset selection problem under parameter uncertainty, where at each time step, the decision maker selects a subset of cardinality from possible ite…