273 research outputs found
Prox-DBRO-VR: A Unified Analysis on Decentralized Byzantine-Resilient Composite Stochastic Optimization with Variance Reduction and Non-Asymptotic Convergence Rates
Decentralized Byzantine-resilient stochastic gradient algorithms resolve
efficiently large-scale optimization problems in adverse conditions, such as
malfunctioning agents, software bugs, and cyber attacks. This paper targets on
handling a class of generic composite optimization problems over multi-agent
cyberphysical systems (CPSs), with the existence of an unknown number of
Byzantine agents. Based on the proximal mapping method, two variance-reduced
(VR) techniques, and a norm-penalized approximation strategy, we propose a
decentralized Byzantine-resilient and proximal-gradient algorithmic framework,
dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only
local computations and communications. To reduce asymptotically the variance
generated by evaluating the noisy stochastic gradients, we incorporate two
localized variance-reduced techniques (SAGA and LSVRG) into Prox-DBRO-VR, to
design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. Via analyzing the contraction
relationships among the gradient-learning error, robust consensus condition,
and optimal gap, the theoretical result demonstrates that both Prox-DBRO-SAGA
and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size,
converge linearly (resp., sub-linearly) inside an error ball around the optimal
solution to the optimization problem under standard assumptions. The trade-offs
between the convergence accuracy and the number of Byzantine agents in both
linear and sub-linear cases are characterized. In simulation, the effectiveness
and practicability of the proposed algorithms are manifested via resolving a
sparse machine-learning problem over multi-agent CPSs under various Byzantine
attacks.Comment: 14 pages, 0 figure
Large Language Models at Work in China's Labor Market
This paper explores the potential impacts of large language models (LLMs) on
the Chinese labor market. We analyze occupational exposure to LLM capabilities
by incorporating human expertise and LLM classifications, following Eloundou et
al. (2023)'s methodology. We then aggregate occupation exposure to the industry
level to obtain industry exposure scores. The results indicate a positive
correlation between occupation exposure and wage levels/experience premiums,
suggesting higher-paying and experience-intensive jobs may face greater
displacement risks from LLM-powered software. The industry exposure scores
align with expert assessments and economic intuitions. We also develop an
economic growth model incorporating industry exposure to quantify the
productivity-employment trade-off from AI adoption. Overall, this study
provides an analytical basis for understanding the labor market impacts of
increasingly capable AI systems in China. Key innovations include the
occupation-level exposure analysis, industry aggregation approach, and economic
modeling incorporating AI adoption and labor market effects. The findings will
inform policymakers and businesses on strategies for maximizing the benefits of
AI while mitigating adverse disruption risks
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