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https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties
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/html/2608.03452v1
2 Membership is decodable beyond the surface forms
4
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/math/new
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New submissions (showing 219 of 219 entries)
[1] arXiv:2608
https://arxiv.org/html/2607.27099v1
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arXiv:2607
https://arxiv.org/html/2607.22511v2
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https://arxiv.org/html/2608.02609v1
A student can type “The king rules the land” and receive a clay tablet bearing the cuneiform equivalent; a museum visitor can render their name in the world’s oldest script; a researcher can back-translate English glosses into Akkadian for data augmentation
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2608.02800v1
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/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/2608.02943v1
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arXiv:2608
https://arxiv.org/html/2608.03504v1
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arXiv:2608
https://arxiv.org/html/2607.24970v2
Said differently, if each application targets one of nn schools randomly, how many applications do I need for each school to receive at least one application
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/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/list/cs.LG/2022-12
2001-2004
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[1] arXiv:2212
https://arxiv.org/html/2607.26405v1
& Optimal Design
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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/html/2412.12213v2
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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/2508.20101v1
Similar forms of volatility propagation arise in environmental and epidemiological contexts, where uncertainty in model predictions, such as pollutant concentrations or disease incidence rates, varies dynamically across both space and time due to complex interactions between spatial covariates, temporal dynamics, and
https://arxiv.org/html/2608.02851v1
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arXiv:2608
https://arxiv.org/html/2403.16413v3
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arXiv:2403