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2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
from the top result below
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2607.22511v2
1 Introduction
Language models can now generate research artifacts—conjectures, proofs, experiments, and complete papers—more rapidly than they can be evaluated
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/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/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/2607.26405v1
After World War II, the stable US economy with its dollar attached to the gold standard made it an attractive peg codified through the 1944 Bretton Woods Agreement
https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2412.12213v2
Importantly, FINN extends beyond liquid option markets: it can be trained directly on historical spot prices to construct coherent option prices and Greeks for assets with no listed options
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/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/html/2403.16413v3
org perpetual non-exclusive license
arXiv:2403
https://arxiv.org/list/cs.DS/new
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New submissions (showing 7 of 7 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.IT/new
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New submissions (showing 11 of 11 entries)
[1] arXiv:2608
https://arxiv.org/html/2607.24970v2
A well-known modification of this algorithm allows schools to consider applications even when they are already full, so that schools can reject previously accepted students in favour of higher-priority late applicants
https://arxiv.org/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.02909v1
Through simulations, we show that this approach recovers estimates close to the true values, even in relatively small samples, while the standard approach can produce substantial bias in practice
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/2407.00575v3
The global performance at NE can be improved by imposing linear constraints on the NE, also known as a generalized Nash equilibrium (GNE)
https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/html/2407.20187v1
If so, can we do this optimally in polynomial time
https://arxiv.org/html/2607.28294v1
Although classical bootstrap consistency can fail when the durations have infinite expectation, we argue that the bootstrap remains valid for percentile and reverse-percentile inference and yields asymptotically normal t-statistics
https://arxiv.org/html/2407.08254v2
These agents can communicate and coordinate with each other, either directly or indirectly, to solve complex tasks that are beyond a single agent’s capabilities
https://arxiv.org/html/2407.06328v2
These games can be viewed as dynamic extensions of conventional population games, with potential applications in multi-robot systems research as demonstrated in [6]
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/2604.05327v4
Under mild conditions, the finite-sample likelihoods—which may themselves be misspecified—can be replaced by a limit experiment involving the Gaussian family as the reference likelihood
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/html/2608.03573v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license
arXiv:2608