12 papers
Online Convex Optimization with Dueling Feedback
Yiyang Lu, Hareshkumar Jadav, Mohammad Pedramfar +2
We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points. While dueling feedb…
Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
Yiyang Lu, Haresh Jadav, Mohammad Pedramfar +2
We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer…
Multi-Armed Sampling Problem and the End of Exploration
Mohammad Pedramfar, Siamak Ravanbakhsh
This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed bandits. Our primary motivation is…
The Role of Symmetry in Optimizing Overparameterized Networks
Kusha Sareen, Mohammad Pedramfar, Sékou-Oumar Kaba +2
Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We analyze weight-space symmet…
-weakly -up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions
Mohammad Pedramfar, Vaneet Aggarwal
Optimizing non-convex functions is a fundamental challenge across machine learning and combinatorial optimization. We introduce and study -weakly -up-concavity, a novel fir…
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…