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A statement can be free of sorry and still be mathematically uninformative: a definition may reduce to True; or an unproven step may be introduced as an axiom that the kernel accepts
from the top result below
https://arxiv.org/html/2607.22511v2
A statement can be free of sorry and still be mathematically uninformative: a definition may reduce to True; or an unproven step may be introduced as an axiom that the kernel accepts
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/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/html/2608.02851v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.LG/2022-12
2001-2004
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[1] arXiv:2212
https://arxiv.org/list/cs.CR/new
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[1] arXiv:2608
https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2511.06451v1
Spectral safety and discrete Green kernel
https://arxiv.org/html/2407.05763v1
However, centralized observers for plants modeled by nonlinear systems satisfying Ho¨¨o\ddot{\mathrm{o}}over¨ start_ARG roman_o end_ARGlder condition can be found in the recent literature (see, e
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.AR/new
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[1] arXiv:2608
https://arxiv.org/html/2607.29442v2
In an asymptotic framework inspired by Rosenbaum2018, we show that the rescaled price process converges to a Bachelier-type semimartingale plus a Gaussian moving average whose kernel is determined explicitly by the trawl function
https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks
https://arxiv.org/html/2407.20441v1
I Introduction
Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy
https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02609v1
edu
Abstract
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world’s oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
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/abs/2608.12700
Those rates come from a single loose test: run the kernel on a few random inputs at one fixed shape and accept it if the output is close to a reference
https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2403.16413v3
org perpetual non-exclusive license
arXiv:2403
https://arxiv.org/html/2407.00419v1
If we make no assumptions about the target population, we can do little more than attempt to mimic observed behavior as closely as possible, reducing the problem to one of imitation learning
https://arxiv.org/html/2607.26405v1
Among other reasons, when bilateral trade between two currencies is moderate or low, neither side can sustain a deep enough market to offer competitive spreads, so traders gravitate toward an intermediate currency with high liquidity
https://arxiv.org/html/2508.20108v2
How can we address distribution shifts in stock price data to improve stock price prediction accuracy
https://arxiv.org/html/2407.10811v1
For real-world solutions, only flow can be reliably collected, whereas common RL methods need more
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/2608.02800v1
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