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20202023
most citedHarms from Increasingly Agentic Algorithmic Systems

124 citations · 142 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY2023★ 124 cited

Harms from Increasingly Agentic Algorithmic Systems

Alan Chan, Rebecca Salganik, Alva Markelius +19

Research in Fairness, Accountability, Transparency, and Ethics (FATE) has established many sources and forms of algorithmic harm, in domains as diverse as health care, finance, pol…

cs.LG2023★ 1 cited

On The Fragility of Learned Reward Functions

Lev McKinney, Yawen Duan, David Krueger +1

Reward functions are notoriously difficult to specify, especially for tasks with complex goals. Reward learning approaches attempt to infer reward functions from human feedback and…

cs.LG2022★ 10 cited

Adversarial Policies Beat Superhuman Go AIs

Tony T. Wang, Adam Gleave, Tom Tseng +8

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our…

cs.CV2021★ 4 cited

TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search

Yawen Duan, Xin Chen, Hang Xu +4

Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existin…

cs.LG2020★ 3 cited

CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search

Xin Chen, Yawen Duan, Zewei Chen +5

Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They…