5 papers · 1 filter
Stochastic -Submodular Bandits with Full Bandit Feedback
Guanyu Nie, Vaneet Aggarwal, Christopher John Quinn
In this paper, we present the first sublinear -regret bounds for online -submodular optimization problems with full-bandit feedback, where is a corresponding offline appr…
From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization
Mohammad Pedramfar, Vaneet Aggarwal
This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various settings, including monotone and non-…
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
Mohammad Pedramfar, Yididiya Y. Nadew, Christopher J. Quinn +1
This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (se…
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Swetha Ganesh, Jiayu Chen, Gugan Thoppe +1
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of th…
A Unified Framework for Analyzing Meta-algorithms in Online Convex Optimization
Mohammad Pedramfar, Vaneet Aggarwal
In this paper, we analyze the problem of online convex optimization in different settings, including different feedback types (full-information/semi-bandit/bandit/etc) in either st…