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For example, if the algorithm chosen by the researcher to estimate Ï„\tau is sufficiently unstable (in the sense of Bousquet and Elisseeff, 2002), they could trim the data until the result becomes statistically significant
from the top result below

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
This version is a work in progress; please feel free to contact me if you find a mistake, think I missed an important contribution, or just want to send a comment

Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license arXiv:2508

You’ve Got to be Efficient: Ambiguity, Misspecification and

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

1 Introduction

https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release) Synthetic risk–neutral generator

Preying on Leveraged ETFsPreliminary and incomplete. This project was

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.

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

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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

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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02872
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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/pdf/2608.03494
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04003
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https://arxiv.org/pdf/2309.08431
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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