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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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5 results for “preemption”

Strategic Irreversible Investmentecon.TH

The paper analyzes strategic irreversible investments with novel dynamic strategies.

problem Tradeoff between preemption incentives and option value of waiting in oligopolistic markets.

method Developed novel Markov perfect equilibrium to handle singular control of optimal investment.

result Simpler strategies lead to a 'preemption trap' with zero net present values.

irreversible investment Markov perfect equilibrium singular control oligopolistic markets preemption incentives

2024-10-23abs ↗pdf ↗

Machine Learning on Volatile Instancescs.LG

Cost-effective strategies for training machine learning models on volatile cloud instances.

problem Expensive distributed SGD training due to resource interruptions.

method Cost-effective strategies for exploiting volatile cloud instances (spot instances) in distributed SGD.

result Quantified the impact of instance preemption on SGD convergence and training time.

machine learning distributed SGD volatility spot instances preemption

2020-03-12abs ↗pdf ↗

Strategic Preemption Under Shared Catastrophic Risk: The Suicide Region and the Race to Artificial General Intelligenceq-fin.RM

New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.

problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.

method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.

result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.

catastrophic risk preemption game artificial general intelligence suicide region welfare distortion

2025-12-08abs ↗pdf ↗

On Preemption and Learning in Stochastic Schedulingcs.LG

Study on scheduling jobs with unknown types, achieving sublinear excess cost.

problem Optimizing job scheduling with unknown job types and varying durations.

method Design of algorithms for non-preemptive and preemptive scenarios, proving lower bounds.

result Preemptive algorithms can significantly outperform non-preemptive ones when job types have distinct durations.

scheduling unknown types sublinear excess cost preemption job durations

2022-05-31abs ↗pdf ↗

Scheduling Jobs with Stochastic Holding Costscs.LG

Study schedules jobs with unknown holding costs to minimize expected cumulative cost.

problem Minimizing expected cumulative holding costs of jobs with unknown parameters.

method Learning-based

cμ

rule scheduling with a preemption phase.

result Achieves near-optimal performance guarantees with nearly matching regret bounds.

job scheduling stochastic costs regret minimization learning algorithms

2021-05-28abs ↗pdf ↗