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δ∈Δ\delta\in\Delta Decision rule The rule that turns estimated values into a recommendation: for example, treat if τ^>0\hat{\tau}>0, target groups with positive estimated gains, rank policies under a budget, or choose the policy with the highest estimated welfare
from the top result below
https://arxiv.org/html/2607.29281v2
Geometric Control under Donor-Target Mismatch
https://arxiv.org/html/2407.01093v1
com/ and GPT-4 OpenAI (2023) could be used to simulate human intelligence
https://arxiv.org/html/2604.05327v4
For these problems, it is therefore always optimal for the decision-maker to proceed as if the likelihood were correctly specified and select the resulting optimal decision rule
https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
https://arxiv.org/list/cs.CV/new
Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/html/2407.16294v1
com 22institutetext: School of Politics and International Relations, University College Dublin, Ireland
22email: graham
https://arxiv.org/html/2608.02854v1
1 Introduction
In decision-making under uncertainty, particularly in finance, insurance, and risk management, a decision maker must evaluate and rank random outcomes
https://arxiv.org/html/2607.26405v1
Specifically, an automated market maker (AMM) is a decentralized exchange protocol that holds pooled reserves of assets that are traded against at prices governed by a mathematical invariant
https://arxiv.org/html/2312.16193v2
1 Introduction
2 Background
2.1 Layer-2 Blockchains
2.2 Project Mariana
3 Related Work and Contribution
4 System Architecture
5 Model
5.1 Automated Market Makers
5.2 Price Impact
6 Simulation
6.1 Data and Parameters
6.2 Results
7
https://arxiv.org/html/2410.19947v2
org perpetual non-exclusive license
arXiv:2410
https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/pdf/2609.18842
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon
https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2104.08691
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https://arxiv.org/src/2006.02924
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https://arxiv.org/list/cs.NA/new
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New submissions (showing 18 of 18 entries)
[1] arXiv:2608
https://arxiv.org/src/2006.03206
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/pdf/2608.02609
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