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Did you mean if com site lemmy would site mylandgroup cz site:arxiv.org?
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https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/pdf/1811.12628
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https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2407.00419v1
Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy
https://arxiv.org/html/2407.00575v3
Even worse, the manager would need to collect the parameters periodically if they are time-varying, and then solve the large-scale optimization problem again
https://arxiv.org/html/2508.08980v3
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arXiv:2508
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2604.05327v4
This version:
I would like to thank Xu Cheng, Frank Diebold, Wayne Gao, George Mailath, and seminar participants at Chicago Booth, Paris Econometrics Seminar, Toulouse School of Economics, and the University of Pennsylvania for valuable discussions and comments that substantially improved this article
https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.04889v1
(2019b); however, in general settings it is not known how to best play against mean-based learners, even if the agent knows everything about the learners, and can predict with certainty what actions they would take as a function of the history of play
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/html/2407.18010v2
Since acting at each available opportunity would accumulate prohibitively large costs, the resulting decision problem is one in which players must make strategic decisions about when to execute actions in addition to their choice of action
https://arxiv.org/pdf/2607.28577v1
) /Title (Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.03063
‘Êõë÷Ù”º›‡@°¤ê:^½ª¾(1ÉtÌãÂ0Íxª™a2ŠX̤6,aJ(–2•B#bZÆ93†CŸÅo’Å4¦XbbÆ5KUĸ�2F8$QŒ÷V Âfø# "RÁÈ^Ex‡WeðU-5CH®�D PᦱÁ;ü%2aþ’ïð—ÆxáRâ�ÃU¤˜$Wqʤ„�ï ¦øB®|‘uŠÈ/' .¥ …4�‚4À©à2N98À�QLIˆ@ƒÂG‘â]ã#Œ�šÒ ìð2 8£ð*"ñ4TS°¤9>‚Âú…àÜ+à
https://arxiv.org/src/2211.10435
’•Ž•ÄCom„²î�Þ4 ߎ÷öÏ(M®W"_xºˆ¥î ;;Bu8çñ¶5'UNã�þ°�;hÿ˜ª&‘ú•H—0«Ô�’P/ÙÐ^�/3°ÇKÊO°¿`õ¨GÌC/ +¢Ó„î3j†õ‚�ˆGIt, ;]"}ö3äÜü·«ÑŠ_wË“sÇÝøú&™s]bæ%â¡á#õ-˜¾âIc:‚9Àë€ÿ�¨áPb¸(RGlŸÎÀ•»+Ì�âGÌGØKÆ~XA/(×…šd C‚bB×UCÜGäž8t¬úˆY[”|ôù�A]ƒì['ê�¤Q²[¯S±SôMÓÜöl`–ßÝ/›Jn'퉪ÍÛ|;žxV|ß¶;~0\„ˆÌh„[†ÈªéÎz¼ö˜°)”G°Õ˜º> ~âìû‘þX
https://arxiv.org/pdf/2607.27188v1
) /Title (Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
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/2608.03883
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard
https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2608.01785
+ÖÑÖ1‡}ª$jšdKmo€Át‹u°ê«bUQÍHF8‘†B™!’0Ή"ÌX¢ —š …rb‰d¡‘ZJ‰ ø‰¦ðÁ‰ÖœPAŒ¡„Jb¹%¸ž;î — HIF(�¬£2fa@â>�$€Î@‘d`˜#!ð•*M }šÃ'è3ÐAŸ1 Ç‚* søÊX ,Ò‚K Êfœ€3œjxð§xD¸†ý¸""Sˆàèà¦ðl‰0–‘H*øN¤Ì€È #•3Q™áD¢8¸,$BBŠ(ÎM”6°nˆ²Š0‚cDfDs
https://arxiv.org/html/2403.16413v3
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arXiv:2403
https://arxiv.org/html/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/cs.DS/new
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https://arxiv.org/list/cs.LG/2022-12
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[1] arXiv:2212
https://arxiv.org/html/2608.02609v1
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https://arxiv.org/html/2607.24970v2
That total number of applications mirrors the classical coupon-collector problem: if each breakfast cereal box contains one of nn equally likely coupons, how many cereal boxes do I need to buy to collect all nn coupons