5 citations · 7 across the 18 of their papers we have counts for
20 papers
The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
Mohammad Pedramfar, Siamak Ravanbakhsh
Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs. Examples include entropy-regularized reinforcement learning, R…
Dec-BFTRL: Squre-Root Regret for Decentralized Online Upper-Linearizable Optimization under Separation Access with Application to Continuous Submodular Maximization
Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal
We study decentralized online optimization of upper-linearizable payoffs over an action set under efficient separation access, with applications to online continuous diminishing-re…
Online Convex Optimization with Dueling Feedback
Yiyang Lu, Hareshkumar Jadav, Mohammad Pedramfar +2
Noisy binary comparison between two candidates is a common interface between human and learning systems, especially in modern large language model (LLM) post-training alignment. We…
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…
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…
-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 first…