activity
20242026
most citedRevisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO2026

Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation

Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik +2

In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we eval…

cs.LG2025

Learning Without Time-Based Embodiment Resets in Soft-Actor Critic

Homayoon Farrahi, A. Rupam Mahmood

When creating new reinforcement learning tasks, practitioners often accelerate the learning process by incorporating into the task several accessory components, such as breaking th…

cs.LG20241 cited

Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning

Mohamed Elsayed, Homayoon Farrahi, Felix Dangel +1

Second-order information is valuable for many applications but challenging to compute. Several works focus on computing or approximating Hessian diagonals, but even this simplifica…

cs.SE2024

Analysis of Marketed versus Not-marketed Mobile App Releases

Maleknaz Nayebi, Homayoon Farrahi, Guenther Ruhe

Market and user characteristics of mobile apps make their release management different from proprietary software products and web services. Despite the wealth of information regard…

cs.SE2024

More Insight from Being More Focused: Analysis of Clustered Market Apps

Maleknaz Nayebi, Homayoon Farrahi, Ada Lee +2

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as…