1 citations · 1 across the 4 of their papers we have counts for
4 papers
Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries
Prarabdh Shukla, Ritik, Suhas Rao +2
With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ("the average Jane") could elicit actionable responses to malicious…
Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshminarayanan Subramanian +1
User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in tur…
Semi-Bandit Learning for Monotone Stochastic Optimization
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan +1
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algor…
Learning-Augmented Dynamic Submodular Maximization
Arpit Agarwal, Eric Balkanski
In dynamic submodular maximization, the goal is to maintain a high-value solution over a sequence of element insertions and deletions with a fast update time. Motivated by large-sc…